From 7073910a4c8b3d1dcbb194fb71c39b614bd82893 Mon Sep 17 00:00:00 2001
From: Joshua Hori <jdhori@gmail.com>
Date: Tue, 28 Apr 2026 21:53:32 -0700
Subject: [PATCH 1/5] update workflow

---
 .github/scripts/check-video-accessibility.py | 282 +++++++++++++++++++
 .github/workflows/a11y.yml                   |  40 ++-
 2 files changed, 307 insertions(+), 15 deletions(-)
 create mode 100755 .github/scripts/check-video-accessibility.py

diff --git a/.github/scripts/check-video-accessibility.py b/.github/scripts/check-video-accessibility.py
new file mode 100755
index 00000000..813d159a
--- /dev/null
+++ b/.github/scripts/check-video-accessibility.py
@@ -0,0 +1,282 @@
+#!/usr/bin/env python3
+
+"""
+Video Accessibility Checker for Jupyter Book HTML
+
+This script scans built HTML files (typically in _build/html)
+and flags accessibility issues related to video embeds.
+
+Why this exists:
+Automated tools like axe-core do not reliably detect:
+- Missing iframe titles
+- Poor iframe labeling
+- Missing transcripts near videos
+- Platform-based caption issues (YouTube, YuJa, Kaltura, etc.)
+- Autoplay and hidden control risks
+
+This script supplements those gaps.
+
+What it checks:
+1. Iframe embeds (YouTube, Vimeo, etc.)
+   - Missing title or aria-label (WCAG 4.1.2)
+   - Generic titles like "video"
+   - Autoplay usage (WCAG 2.2.2)
+   - Hidden controls (WCAG 2.1.1)
+   - Missing nearby transcript/caption indicators (WCAG 1.2.x)
+
+2. Native <video> elements
+   - Missing controls (WCAG 2.1.1)
+   - Missing captions/subtitles (WCAG 1.2.2)
+   - Missing description track (WCAG 1.2.3 / 1.2.5)
+   - Autoplay behavior
+   - Missing nearby transcript/audio description
+
+Limitations:
+- Cannot confirm captions exist inside third-party platforms
+- Cannot evaluate caption quality
+- Cannot detect audio description content, only presence hints
+
+Usage:
+python check-video-accessibility.py _build/html
+"""
+
+import sys
+import os
+import threading
+import http.server
+import socketserver
+import urllib.parse
+from pathlib import Path
+from urllib.parse import urlparse, parse_qs
+from bs4 import BeautifulSoup
+from playwright.sync_api import sync_playwright
+
+def start_server(directory, port=8000):
+    class Handler(http.server.SimpleHTTPRequestHandler):
+        def __init__(self, *args, **kwargs):
+            super().__init__(*args, directory=directory, **kwargs)
+        def log_message(self, format, *args):
+            pass # Suppress logging
+    httpd = socketserver.TCPServer(("", port), Handler)
+    thread = threading.Thread(target=httpd.serve_forever, daemon=True)
+    thread.start()
+    return httpd
+
+VIDEO_DOMAINS = [
+    "youtube.com",
+    "youtu.be",
+    "youtube-nocookie.com",
+    "vimeo.com",
+    "panopto",
+    "kaltura",
+    "yuja",
+    "media.ucdavis.edu",
+    "video.ucdavis.edu",
+]
+
+SUPPORT_WORDS = [
+    "transcript",
+    "caption",
+    "captions",
+    "subtitles",
+    "audio description",
+    "described video",
+    "media alternative",
+]
+
+GENERIC_TITLES = [
+    "",
+    "video",
+    "youtube",
+    "youtube video",
+    "iframe",
+    "embedded content",
+    "player",
+    "video player",
+]
+
+def is_video_iframe(src):
+    return any(domain in src.lower() for domain in VIDEO_DOMAINS)
+
+def nearby_text(element):
+    parent = element.find_parent()
+    if not parent:
+        return ""
+    return parent.get_text(" ", strip=True).lower()
+
+def has_support_text(element):
+    text = nearby_text(element)
+    return any(word in text for word in SUPPORT_WORDS)
+
+def get_params(src):
+    return parse_qs(urlparse(src).query)
+
+def annotate_error(message):
+    print(f"::error::{message}")
+
+def annotate_warning(message):
+    print(f"::warning::{message}")
+
+def check_iframe(file, iframe):
+    errors = []
+    warnings = []
+
+    src = iframe.get("src", "").strip()
+    if not src or not is_video_iframe(src):
+        return errors, warnings
+
+    title = iframe.get("title", "").strip()
+    aria = iframe.get("aria-label", "").strip()
+    name = title or aria
+
+    if not name:
+        errors.append(f"{file}: Missing iframe title/aria-label → {src}")
+
+    elif name.lower() in GENERIC_TITLES:
+        warnings.append(f"{file}: Generic iframe title '{name}' → {src}")
+
+    if not has_support_text(iframe):
+        warnings.append(f"{file}: No transcript/caption context near video → {src}")
+
+    params = get_params(src)
+
+    if params.get("autoplay", ["0"])[0] == "1":
+        warnings.append(f"{file}: Autoplay detected → {src}")
+
+    if params.get("controls", ["1"])[0] == "0":
+        errors.append(f"{file}: Controls disabled → {src}")
+
+    if "youtube" in src:
+        if not (
+            params.get("cc_load_policy", ["0"])[0] == "1"
+            or "caption" in nearby_text(iframe)
+        ):
+            warnings.append(f"{file}: YouTube embed lacks caption hint → {src}")
+
+    if "autoplay" in iframe.get("allow", "").lower():
+        warnings.append(f"{file}: iframe allows autoplay → {src}")
+
+    return errors, warnings
+
+def check_video(file, video):
+    errors = []
+    warnings = []
+
+    if not video.has_attr("controls"):
+        errors.append(f"{file}: <video> missing controls")
+
+    if video.has_attr("autoplay"):
+        warnings.append(f"{file}: <video> uses autoplay")
+
+    tracks = video.find_all("track")
+
+    has_captions = any(
+        t.get("kind", "").lower() in ["captions", "subtitles"]
+        for t in tracks
+    )
+
+    has_descriptions = any(
+        t.get("kind", "").lower() == "descriptions"
+        for t in tracks
+    )
+
+    if not has_captions:
+        errors.append(f"{file}: <video> missing captions/subtitles")
+
+    if not has_descriptions:
+        warnings.append(f"{file}: <video> missing description track")
+
+    if not has_support_text(video):
+        warnings.append(f"{file}: <video> missing transcript/audio description context")
+
+    return errors, warnings
+
+def write_summary(errors, warnings):
+    path = os.environ.get("GITHUB_STEP_SUMMARY")
+    if not path:
+        return
+
+    with open(path, "a") as f:
+        f.write("# Video Accessibility Report\n\n")
+
+        if errors:
+            f.write("## ❌ Required Fixes\n")
+            for e in errors:
+                f.write(f"- {e}\n")
+
+        if warnings:
+            f.write("\n## ⚠️ Review Items\n")
+            for w in warnings:
+                f.write(f"- {w}\n")
+
+        f.write("\n## Fix Guide\n")
+        f.write("| Issue | Fix |\n")
+        f.write("|------|-----|\n")
+        f.write("| Missing iframe title | Add descriptive title |\n")
+        f.write("| Generic title | Replace with meaningful description |\n")
+        f.write("| Missing captions | Add captions/subtitles |\n")
+        f.write("| Missing transcript | Add transcript near video |\n")
+        f.write("| Autoplay | Remove autoplay |\n")
+        f.write("| No controls | Enable controls |\n")
+
+def main():
+    if len(sys.argv) != 2:
+        print("Usage: script.py <html-dir>")
+        sys.exit(2)
+
+    root = Path(sys.argv[1]).resolve()
+    if not root.exists():
+        print("Directory not found")
+        sys.exit(2)
+
+    all_errors = []
+    all_warnings = []
+
+    # Start a local HTTP server on port 8000
+    start_server(str(root), port=8000)
+
+    with sync_playwright() as p:
+        browser = p.chromium.launch()
+        page = browser.new_page()
+
+        for file in root.rglob("*.html"):
+            try:
+                rel_path = file.relative_to(root).as_posix()
+                url = f"http://localhost:8000/{urllib.parse.quote(rel_path)}"
+                page.goto(url)
+                page.wait_for_load_state("networkidle", timeout=3000)
+            except Exception:
+                pass
+                
+            html = page.content()
+            soup = BeautifulSoup(html, "lxml")
+
+            for iframe in soup.find_all("iframe"):
+                e, w = check_iframe(file.name, iframe)
+                all_errors.extend(e)
+                all_warnings.extend(w)
+
+            for video in soup.find_all("video"):
+                e, w = check_video(file.name, video)
+                all_errors.extend(e)
+                all_warnings.extend(w)
+                
+        browser.close()
+
+    for e in all_errors:
+        annotate_error(e)
+
+    for w in all_warnings:
+        annotate_warning(w)
+
+    write_summary(all_errors, all_warnings)
+
+    if all_errors:
+        print("\nFix required issues above.\n")
+        sys.exit(1)
+
+    print("Checks passed.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/.github/workflows/a11y.yml b/.github/workflows/a11y.yml
index 3cbb0ac1..e1b7804b 100644
--- a/.github/workflows/a11y.yml
+++ b/.github/workflows/a11y.yml
@@ -16,57 +16,67 @@ jobs:
   axe-check:
     runs-on: ubuntu-latest
     steps:
+      # 1. Checkout repo
       - name: Checkout code
         uses: actions/checkout@v4
 
-      # 1. Setup Python & Build the Book
+      # 2. Set up Python
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
           python-version: '3.11'
           cache: 'pip'
-          
+
+      # 3. Install Python dependencies
       - name: Install Python dependencies
-        run: pip install -r requirements.txt
+        run: |
+          pip install -r requirements.txt
+          pip install beautifulsoup4 lxml playwright
+          playwright install chromium --with-deps
 
+      # 4. Build the Jupyter Book HTML
       - name: Build Jupyter Book
         run: jupyter-book build --html
 
-      # 2. Setup Node environment for Axe
+      # 5. Run your custom video accessibility checker
+      # This checks the generated HTML files in _build/html
+      - name: Check video embeds for accessibility
+        run: python .github/scripts/check-video-accessibility.py _build/html
+
+      # 6. Set up Node for axe
       - name: Setup Node.js
         uses: actions/setup-node@v4
         with:
           node-version: '20'
 
+      # 7. Install axe and local server tools
       - name: Install Axe CLI and HTTP Server
-        run: npm install -g @axe-core/cli http-server
+        run: npm install -g @axe-core/cli http-server wait-on browser-driver-manager
 
+      # 8. Install matching Chrome driver
       - name: Get Matching Chrome version
         run: npx browser-driver-manager install chrome
 
-      # 3. Serve, Discover URLs, and Run Axe
+      # 9. Serve the built site and run axe
       - name: Run Axe Checks
         run: |
-          # A. Create a subdirectory structure that matches the BASE_URL
           mkdir -p staging${BASE_URL}
           cp -r _build/html/* staging${BASE_URL}/
-          
-          # B. Start a local server in the background
+
           npx http-server staging -p 8080 -s &
           npx wait-on http://localhost:8080${BASE_URL}/
-          
-          # C. Generate the URL list from the actual build artifacts
+
           cd _build/html
-          URLS=$(find . -name "*.html" -not -path "*/build/*" | sed 's|^\./||' | sed "s|^|http://localhost:8080${BASE_URL}/|" | tr '\n' ' ')          
-          
-          # D. Run Axe CLI on the discovered URLs
+          URLS=$(find . -name "*.html" -not -path "*/build/*" | sed 's|^\./||' | sed "s|^|http://localhost:8080${BASE_URL}/|" | tr '\n' ' ')
+
           axe $URLS \
             --tags wcag2a,wcag2aa,wcag21a,wcag21aa \
             --save axe-report.json \
             --exit
 
+      # 10. Upload axe report
       - name: Upload Accessibility Report
-        if: always() 
+        if: always()
         uses: actions/upload-artifact@v4
         with:
           name: axe-report

From b1218659583a29954da2736b4d12e5c164e42d2c Mon Sep 17 00:00:00 2001
From: Joshua Hori <jdhori@gmail.com>
Date: Tue, 28 Apr 2026 22:06:41 -0700
Subject: [PATCH 2/5] feat: auto-inject iframe titles using oEmbed API

---
 .github/scripts/check-video-accessibility.py | 64 ++++++++++++++++++++
 1 file changed, 64 insertions(+)

diff --git a/.github/scripts/check-video-accessibility.py b/.github/scripts/check-video-accessibility.py
index 813d159a..5da0e690 100755
--- a/.github/scripts/check-video-accessibility.py
+++ b/.github/scripts/check-video-accessibility.py
@@ -50,6 +50,8 @@
 from urllib.parse import urlparse, parse_qs
 from bs4 import BeautifulSoup
 from playwright.sync_api import sync_playwright
+import urllib.request
+import json
 
 def start_server(directory, port=8000):
     class Handler(http.server.SimpleHTTPRequestHandler):
@@ -117,6 +119,43 @@ def annotate_error(message):
 def annotate_warning(message):
     print(f"::warning::{message}")
 
+def fetch_video_title(src):
+    try:
+        if "youtube" in src.lower() or "youtu.be" in src.lower():
+            video_id = None
+            if "/embed/" in src:
+                video_id = src.split("/embed/")[1].split("?")[0]
+            elif "v=" in src:
+                video_id = parse_qs(urlparse(src).query).get("v", [None])[0]
+            elif "youtu.be/" in src:
+                video_id = src.split("youtu.be/")[1].split("?")[0]
+
+            if not video_id:
+                return None
+
+            url = f"https://www.youtube.com/oembed?url=http://www.youtube.com/watch?v={video_id}&format=json"
+            req = urllib.request.Request(url, headers={'User-Agent': 'Mozilla/5.0'})
+            with urllib.request.urlopen(req, timeout=5) as response:
+                data = json.loads(response.read().decode())
+                return data.get("title")
+        
+        elif "vimeo.com" in src.lower():
+            video_id = None
+            if "/video/" in src:
+                video_id = src.split("/video/")[1].split("?")[0]
+            
+            if not video_id:
+                return None
+                
+            url = f"https://vimeo.com/api/oembed.json?url=https%3A%2F%2Fvimeo.com%2F{video_id}"
+            req = urllib.request.Request(url, headers={'User-Agent': 'Mozilla/5.0'})
+            with urllib.request.urlopen(req, timeout=5) as response:
+                data = json.loads(response.read().decode())
+                return data.get("title")
+    except Exception:
+        return None
+    return None
+
 def check_iframe(file, iframe):
     errors = []
     warnings = []
@@ -229,6 +268,31 @@ def main():
         print("Directory not found")
         sys.exit(2)
 
+    print("Auto-fixing missing iframe titles...")
+    for file in root.rglob("*.html"):
+        try:
+            with open(file, 'r', encoding='utf-8') as f:
+                static_html = f.read()
+            static_soup = BeautifulSoup(static_html, "lxml")
+            modified = False
+            for iframe in static_soup.find_all("iframe"):
+                src = iframe.get("src", "").strip()
+                if is_video_iframe(src):
+                    title = iframe.get("title", "").strip()
+                    aria = iframe.get("aria-label", "").strip()
+                    name = title or aria
+                    if not name or name.lower() in GENERIC_TITLES:
+                        fetched_title = fetch_video_title(src)
+                        if fetched_title:
+                            iframe["title"] = fetched_title
+                            modified = True
+                            print(f"  Fixed {file.name}: Added title '{fetched_title}'")
+            if modified:
+                with open(file, 'w', encoding='utf-8') as f:
+                    f.write(str(static_soup))
+        except Exception:
+            pass
+
     all_errors = []
     all_warnings = []
 

From a2407eeb117f8aa971f92d996ed850373089361d Mon Sep 17 00:00:00 2001
From: Joshua Hori <jdhori@gmail.com>
Date: Tue, 28 Apr 2026 22:42:18 -0700
Subject: [PATCH 3/5] fix: auto-fix iframe titles in JSON instead of HTML

---
 .github/scripts/check-video-accessibility.py | 58 +++++++++++++-------
 1 file changed, 39 insertions(+), 19 deletions(-)

diff --git a/.github/scripts/check-video-accessibility.py b/.github/scripts/check-video-accessibility.py
index 5da0e690..c5233c14 100755
--- a/.github/scripts/check-video-accessibility.py
+++ b/.github/scripts/check-video-accessibility.py
@@ -258,6 +258,39 @@ def write_summary(errors, warnings):
         f.write("| Autoplay | Remove autoplay |\n")
         f.write("| No controls | Enable controls |\n")
 
+def process_json_node(node, file_name):
+    modified = False
+    if isinstance(node, dict):
+        if "data" in node and isinstance(node["data"], dict) and "text/html" in node["data"] and "content" in node["data"]["text/html"]:
+            html_str = node["data"]["text/html"]["content"]
+            if "<iframe" in html_str.lower():
+                soup = BeautifulSoup(html_str, "html.parser")
+                iframe_modified = False
+                for iframe in soup.find_all("iframe"):
+                    src = iframe.get("src", "").strip()
+                    title = iframe.get("title", "").strip()
+                    aria = iframe.get("aria-label", "").strip()
+                    name = title or aria
+                    if not name or name.lower() in GENERIC_TITLES:
+                        if "youtube" in src.lower() or "vimeo" in src.lower():
+                            t = fetch_video_title(src)
+                            if t:
+                                iframe["title"] = t
+                                iframe_modified = True
+                                print(f"  Fixed {file_name}: Added title '{t}'")
+                if iframe_modified:
+                    node["data"]["text/html"]["content"] = str(soup)
+                    modified = True
+        
+        for k, v in node.items():
+            if process_json_node(v, file_name):
+                modified = True
+    elif isinstance(node, list):
+        for item in node:
+            if process_json_node(item, file_name):
+                modified = True
+    return modified
+
 def main():
     if len(sys.argv) != 2:
         print("Usage: script.py <html-dir>")
@@ -268,28 +301,15 @@ def main():
         print("Directory not found")
         sys.exit(2)
 
-    print("Auto-fixing missing iframe titles...")
-    for file in root.rglob("*.html"):
+    print("Auto-fixing missing iframe titles in JSON files...")
+    for file in root.rglob("*.json"):
         try:
             with open(file, 'r', encoding='utf-8') as f:
-                static_html = f.read()
-            static_soup = BeautifulSoup(static_html, "lxml")
-            modified = False
-            for iframe in static_soup.find_all("iframe"):
-                src = iframe.get("src", "").strip()
-                if is_video_iframe(src):
-                    title = iframe.get("title", "").strip()
-                    aria = iframe.get("aria-label", "").strip()
-                    name = title or aria
-                    if not name or name.lower() in GENERIC_TITLES:
-                        fetched_title = fetch_video_title(src)
-                        if fetched_title:
-                            iframe["title"] = fetched_title
-                            modified = True
-                            print(f"  Fixed {file.name}: Added title '{fetched_title}'")
-            if modified:
+                data = json.load(f)
+            
+            if process_json_node(data, file.name):
                 with open(file, 'w', encoding='utf-8') as f:
-                    f.write(str(static_soup))
+                    json.dump(data, f, separators=(',', ':'))
         except Exception:
             pass
 

From e0772d00c94ee8b4eb1bfc3cdd329da3f306e6b4 Mon Sep 17 00:00:00 2001
From: Joshua Hori <jdhori@gmail.com>
Date: Tue, 28 Apr 2026 22:54:52 -0700
Subject: [PATCH 4/5] fix: permanently inject video titles into source
 notebooks

---
 .github/scripts/check-video-accessibility.py  | 84 -------------------
 .../Chapter_01/03_Collisions_in_Hashing.ipynb |  4 +-
 content/Chapter_01/04_Birthday_Problem.ipynb  |  4 +-
 .../05_An_Exponential_Approximation.ipynb     |  2 +-
 content/Chapter_02/01_Addition.ipynb          |  4 +-
 content/Chapter_02/03_Multiplication.ipynb    |  4 +-
 .../01_Functions_on_an_Outcome_Space.ipynb    |  4 +-
 content/Chapter_03/02_Distributions.ipynb     |  6 +-
 content/Chapter_03/03_Equality.ipynb          |  2 +-
 .../Chapter_04/01_Joint_Distributions.ipynb   |  4 +-
 .../03_Marginal_Distributions.ipynb           |  2 +-
 .../04_Conditional_Distributions.ipynb        |  4 +-
 .../05_Dependence_and_Independence.ipynb      |  2 +-
 .../01_Bounding_the_Chance_of_a_Union.ipynb   |  2 +-
 .../Chapter_05/02_Inclusion_Exclusion.ipynb   |  6 +-
 .../Chapter_05/03_The_Matching_Problem.ipynb  |  4 +-
 .../04_Sampling_Without_Replacement.ipynb     |  2 +-
 .../Chapter_06/01_Binomial_Distribution.ipynb |  4 +-
 content/Chapter_06/05_Odds_Ratios.ipynb       |  6 +-
 .../Chapter_06/06_Law_of_Small_Numbers.ipynb  |  4 +-
 .../Chapter_07/01_Poisson_Distribution.ipynb  |  4 +-
 .../02_Poissonizing_the_Binomial.ipynb        |  6 +-
 content/Chapter_08/01_Definition.ipynb        |  4 +-
 .../02_Applying_the_Definition.ipynb          |  6 +-
 .../03_Expectations_of_Functions.ipynb        |  4 +-
 content/Chapter_08/04_Additivity.ipynb        |  4 +-
 .../Chapter_08/05_Method_of_Indicators.ipynb  |  6 +-
 .../01_Probability_by_Conditioning.ipynb      |  2 +-
 .../02_Expectation_by_Conditioning.ipynb      |  4 +-
 .../03_Expected_Waiting_Times.ipynb           |  4 +-
 content/Chapter_10/01_Transitions.ipynb       |  6 +-
 .../Chapter_10/02_Deconstructing_Chains.ipynb |  4 +-
 content/Chapter_10/03_Long_Run_Behavior.ipynb |  6 +-
 .../01_Balance_and_Detailed_Balance.ipynb     |  6 +-
 content/Chapter_11/02_Code_Breaking.ipynb     | 12 +--
 .../Chapter_11/03_Metropolis_Algorithm.ipynb  |  4 +-
 content/Chapter_12/01_Definition.ipynb        |  6 +-
 .../02_Prediction_and_Estimation.ipynb        |  2 +-
 content/Chapter_12/03_Bounds.ipynb            |  6 +-
 content/Chapter_13/01_Covariance.ipynb        |  4 +-
 .../02_Properties_of_Covariance.ipynb         |  6 +-
 .../03_Sums_of_Independent_Variables.ipynb    |  6 +-
 .../04_Symmetry_and_Indicators.ipynb          |  6 +-
 .../01_Exact_Distribution_of_a_Sum.ipynb      |  4 +-
 content/Chapter_14/02_PGFs_in_NumPy.ipynb     |  4 +-
 .../Chapter_14/03_Central_Limit_Theorem.ipynb |  4 +-
 content/Chapter_14/05_The_Sample_Mean.ipynb   |  4 +-
 .../Chapter_14/06_Confidence_Intervals.ipynb  |  6 +-
 content/Chapter_15/01_Density_and_CDF.ipynb   |  4 +-
 .../02_The_Meaning_of_Density.ipynb           |  2 +-
 content/Chapter_15/03_Expectation.ipynb       |  2 +-
 .../04_Exponential_Distribution.ipynb         |  4 +-
 .../01_Linear_Transformations.ipynb           |  2 +-
 .../Chapter_16/02_Monotone_Functions.ipynb    |  2 +-
 .../01_Probabilities_and_Expectations.ipynb   | 10 +--
 content/Chapter_17/02_Independence.ipynb      |  2 +-
 ...3_Marginal_and_Conditional_Densities.ipynb |  4 +-
 ...ta_Densities_with_Integer_Parameters.ipynb |  2 +-
 .../01_Standard_Normal_Basics.ipynb           |  4 +-
 content/Chapter_18/03_The_Gamma_Family.ipynb  |  2 +-
 .../04_Chi_Squared_Distributions.ipynb        |  2 +-
 .../02_Moment_Generating_Functions.ipynb      |  4 +-
 .../03_MGFs_Normal_and_the_CLT.ipynb          |  2 +-
 content/Chapter_19/04_Chernoff_Bound.ipynb    |  2 +-
 .../Chapter_20/01_Maximum_Likelihood.ipynb    |  4 +-
 .../Chapter_20/03_Prior_and_Posterior.ipynb   |  2 +-
 .../01_Updating_and_Prediction.ipynb          |  4 +-
 .../02_Beta_Binomial_Distribution.ipynb       |  2 +-
 ...1_Conditional_Expectation_Projection.ipynb |  2 +-
 .../02_Least_Squares_Predictor.ipynb          |  4 +-
 .../03_Variance_by_Conditioning.ipynb         |  4 +-
 content/Chapter_22/04_Examples.ipynb          |  2 +-
 content/Chapter_23/01_Random_Vectors.ipynb    |  6 +-
 .../02_Multivariate_Normal_Vectors.ipynb      |  2 +-
 .../03_Multivariate_Normal_Density.ipynb      | 12 +--
 content/Chapter_23/04_Independence.ipynb      |  2 +-
 .../Chapter_24/01_Linear_Least_Squares.ipynb  |  2 +-
 .../02_Bivariate_Normal_Distribution.ipynb    |  4 +-
 .../03_Regression_and_Bivariate_Normal.ipynb  |  2 +-
 .../Chapter_24/04_Regression_Equation.ipynb   |  6 +-
 .../Chapter_25/04_Multiple_Regression.ipynb   |  4 +-
 81 files changed, 162 insertions(+), 246 deletions(-)

diff --git a/.github/scripts/check-video-accessibility.py b/.github/scripts/check-video-accessibility.py
index c5233c14..813d159a 100755
--- a/.github/scripts/check-video-accessibility.py
+++ b/.github/scripts/check-video-accessibility.py
@@ -50,8 +50,6 @@
 from urllib.parse import urlparse, parse_qs
 from bs4 import BeautifulSoup
 from playwright.sync_api import sync_playwright
-import urllib.request
-import json
 
 def start_server(directory, port=8000):
     class Handler(http.server.SimpleHTTPRequestHandler):
@@ -119,43 +117,6 @@ def annotate_error(message):
 def annotate_warning(message):
     print(f"::warning::{message}")
 
-def fetch_video_title(src):
-    try:
-        if "youtube" in src.lower() or "youtu.be" in src.lower():
-            video_id = None
-            if "/embed/" in src:
-                video_id = src.split("/embed/")[1].split("?")[0]
-            elif "v=" in src:
-                video_id = parse_qs(urlparse(src).query).get("v", [None])[0]
-            elif "youtu.be/" in src:
-                video_id = src.split("youtu.be/")[1].split("?")[0]
-
-            if not video_id:
-                return None
-
-            url = f"https://www.youtube.com/oembed?url=http://www.youtube.com/watch?v={video_id}&format=json"
-            req = urllib.request.Request(url, headers={'User-Agent': 'Mozilla/5.0'})
-            with urllib.request.urlopen(req, timeout=5) as response:
-                data = json.loads(response.read().decode())
-                return data.get("title")
-        
-        elif "vimeo.com" in src.lower():
-            video_id = None
-            if "/video/" in src:
-                video_id = src.split("/video/")[1].split("?")[0]
-            
-            if not video_id:
-                return None
-                
-            url = f"https://vimeo.com/api/oembed.json?url=https%3A%2F%2Fvimeo.com%2F{video_id}"
-            req = urllib.request.Request(url, headers={'User-Agent': 'Mozilla/5.0'})
-            with urllib.request.urlopen(req, timeout=5) as response:
-                data = json.loads(response.read().decode())
-                return data.get("title")
-    except Exception:
-        return None
-    return None
-
 def check_iframe(file, iframe):
     errors = []
     warnings = []
@@ -258,39 +219,6 @@ def write_summary(errors, warnings):
         f.write("| Autoplay | Remove autoplay |\n")
         f.write("| No controls | Enable controls |\n")
 
-def process_json_node(node, file_name):
-    modified = False
-    if isinstance(node, dict):
-        if "data" in node and isinstance(node["data"], dict) and "text/html" in node["data"] and "content" in node["data"]["text/html"]:
-            html_str = node["data"]["text/html"]["content"]
-            if "<iframe" in html_str.lower():
-                soup = BeautifulSoup(html_str, "html.parser")
-                iframe_modified = False
-                for iframe in soup.find_all("iframe"):
-                    src = iframe.get("src", "").strip()
-                    title = iframe.get("title", "").strip()
-                    aria = iframe.get("aria-label", "").strip()
-                    name = title or aria
-                    if not name or name.lower() in GENERIC_TITLES:
-                        if "youtube" in src.lower() or "vimeo" in src.lower():
-                            t = fetch_video_title(src)
-                            if t:
-                                iframe["title"] = t
-                                iframe_modified = True
-                                print(f"  Fixed {file_name}: Added title '{t}'")
-                if iframe_modified:
-                    node["data"]["text/html"]["content"] = str(soup)
-                    modified = True
-        
-        for k, v in node.items():
-            if process_json_node(v, file_name):
-                modified = True
-    elif isinstance(node, list):
-        for item in node:
-            if process_json_node(item, file_name):
-                modified = True
-    return modified
-
 def main():
     if len(sys.argv) != 2:
         print("Usage: script.py <html-dir>")
@@ -301,18 +229,6 @@ def main():
         print("Directory not found")
         sys.exit(2)
 
-    print("Auto-fixing missing iframe titles in JSON files...")
-    for file in root.rglob("*.json"):
-        try:
-            with open(file, 'r', encoding='utf-8') as f:
-                data = json.load(f)
-            
-            if process_json_node(data, file.name):
-                with open(file, 'w', encoding='utf-8') as f:
-                    json.dump(data, f, separators=(',', ':'))
-        except Exception:
-            pass
-
     all_errors = []
     all_warnings = []
 
diff --git a/content/Chapter_01/03_Collisions_in_Hashing.ipynb b/content/Chapter_01/03_Collisions_in_Hashing.ipynb
index 8f21a1bf..615a1181 100644
--- a/content/Chapter_01/03_Collisions_in_Hashing.ipynb
+++ b/content/Chapter_01/03_Collisions_in_Hashing.ipynb
@@ -78,7 +78,7 @@
     "#| label: vid-prob-model\n",
     "#| echo: false\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"uFLsmVFcHXw\")"
+    "YouTubeVideo(\"uFLsmVFcHXw\", extras=['title=\"Probability Model\"'])"
    ]
   },
   {
@@ -170,7 +170,7 @@
    "source": [
     "#| label: vid-collision-prob\n",
     "#| echo: false\n",
-    "YouTubeVideo('cD26s3DW-J8')"
+    "YouTubeVideo('cD26s3DW-J8', extras=['title=\"Collision Probability\"'])"
    ]
   },
   {
diff --git a/content/Chapter_01/04_Birthday_Problem.ipynb b/content/Chapter_01/04_Birthday_Problem.ipynb
index c1148d3c..3298c1d1 100644
--- a/content/Chapter_01/04_Birthday_Problem.ipynb
+++ b/content/Chapter_01/04_Birthday_Problem.ipynb
@@ -103,7 +103,7 @@
     "#| label: vid-bday-assum\n",
     "#| echo: false\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('A88MJdZLe3A')"
+    "YouTubeVideo('A88MJdZLe3A', extras=['title=\"Birthday Assumptions\"'])"
    ]
   },
   {
@@ -191,7 +191,7 @@
     "#| label: vid-note-code\n",
     "#| echo: false\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"ci7bPVOpfsk\")"
+    "YouTubeVideo(\"ci7bPVOpfsk\", extras=['title=\"Notes on the Code\"'])"
    ]
   },
   {
diff --git a/content/Chapter_01/05_An_Exponential_Approximation.ipynb b/content/Chapter_01/05_An_Exponential_Approximation.ipynb
index 05c0b25c..3a9e7e6c 100644
--- a/content/Chapter_01/05_An_Exponential_Approximation.ipynb
+++ b/content/Chapter_01/05_An_Exponential_Approximation.ipynb
@@ -97,7 +97,7 @@
     "#| label: vid-approx-collide\n",
     "#| echo: false\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"rUhunl890KM\")"
+    "YouTubeVideo(\"rUhunl890KM\", extras=['title=\"Approximate Chance of Collision\"'])"
    ]
   },
   {
diff --git a/content/Chapter_02/01_Addition.ipynb b/content/Chapter_02/01_Addition.ipynb
index 39aed6d0..459d5842 100644
--- a/content/Chapter_02/01_Addition.ipynb
+++ b/content/Chapter_02/01_Addition.ipynb
@@ -283,7 +283,7 @@
     "#| label: vid-axioms\n",
     "#| echo: false\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"KaOriB3Zep8\")"
+    "YouTubeVideo(\"KaOriB3Zep8\", extras=['title=\"Axioms\"'])"
    ]
   },
   {
@@ -434,7 +434,7 @@
     "#| label: vid-diff\n",
     "#| echo: false\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"eEoogMY8z60\")"
+    "YouTubeVideo(\"eEoogMY8z60\", extras=['title=\"Differences\"'])"
    ]
   },
   {
diff --git a/content/Chapter_02/03_Multiplication.ipynb b/content/Chapter_02/03_Multiplication.ipynb
index 2176acc3..8bfa5e85 100644
--- a/content/Chapter_02/03_Multiplication.ipynb
+++ b/content/Chapter_02/03_Multiplication.ipynb
@@ -188,7 +188,7 @@
    "source": [
     "#| label: vid-frac-frac\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"Nd_gWqoMah4\")"
+    "YouTubeVideo(\"Nd_gWqoMah4\", extras=['title=\"Fractions of Fractions\"'])"
    ]
   },
   {
@@ -284,7 +284,7 @@
    ],
    "source": [
     "#| label: vid-cond-prob\n",
-    "YouTubeVideo(\"YE9p2cprkOI\")"
+    "YouTubeVideo(\"YE9p2cprkOI\", extras=['title=\"Conditional Probability\"'])"
    ]
   },
   {
diff --git a/content/Chapter_03/01_Functions_on_an_Outcome_Space.ipynb b/content/Chapter_03/01_Functions_on_an_Outcome_Space.ipynb
index 3f3c8cc6..99286beb 100644
--- a/content/Chapter_03/01_Functions_on_an_Outcome_Space.ipynb
+++ b/content/Chapter_03/01_Functions_on_an_Outcome_Space.ipynb
@@ -397,7 +397,7 @@
    "source": [
     "#| label: vid-rv\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"h8k0M1ubIxk\")"
+    "YouTubeVideo(\"h8k0M1ubIxk\", extras=['title=\"Random Variables\"'])"
    ]
   },
   {
@@ -663,7 +663,7 @@
    ],
    "source": [
     "#| label: vid-event-5\n",
-    "YouTubeVideo(\"OJZDPWSKIbw\")"
+    "YouTubeVideo(\"OJZDPWSKIbw\", extras=['title=\"Events Determined by S\"'])"
    ]
   },
   {
diff --git a/content/Chapter_03/02_Distributions.ipynb b/content/Chapter_03/02_Distributions.ipynb
index d154a5fe..0091ffaa 100644
--- a/content/Chapter_03/02_Distributions.ipynb
+++ b/content/Chapter_03/02_Distributions.ipynb
@@ -183,7 +183,7 @@
    "source": [
     "#| label: vid-dist-rv\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"7ZznCAYa48Q\")"
+    "YouTubeVideo(\"7ZznCAYa48Q\", extras=['title=\"Distribution of a Random Variable\"'])"
    ]
   },
   {
@@ -535,7 +535,7 @@
    ],
    "source": [
     "#| label: vid-prob-hist\n",
-    "YouTubeVideo(\"jOLQGfccbhs\")"
+    "YouTubeVideo(\"jOLQGfccbhs\", extras=['title=\"Probability Histogram\"'])"
    ]
   },
   {
@@ -717,7 +717,7 @@
    ],
    "source": [
     "#| label: vid-notation-calc\n",
-    "YouTubeVideo(\"QiTc-HKnlFc\")"
+    "YouTubeVideo(\"QiTc-HKnlFc\", extras=['title=\"Notation and Calculation\"'])"
    ]
   },
   {
diff --git a/content/Chapter_03/03_Equality.ipynb b/content/Chapter_03/03_Equality.ipynb
index 2d552a26..21b087b2 100644
--- a/content/Chapter_03/03_Equality.ipynb
+++ b/content/Chapter_03/03_Equality.ipynb
@@ -94,7 +94,7 @@
    "source": [
     "#| label: vid-two-eq\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"Z-_RSmBktHM\")"
+    "YouTubeVideo(\"Z-_RSmBktHM\", extras=['title=\"Two Kinds of Equality\"'])"
    ]
   },
   {
diff --git a/content/Chapter_04/01_Joint_Distributions.ipynb b/content/Chapter_04/01_Joint_Distributions.ipynb
index 7be09f86..b7a68e75 100644
--- a/content/Chapter_04/01_Joint_Distributions.ipynb
+++ b/content/Chapter_04/01_Joint_Distributions.ipynb
@@ -107,7 +107,7 @@
    "source": [
     "#| label: vid-joint-dist\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"5utr7G2OXXA\")"
+    "YouTubeVideo(\"5utr7G2OXXA\", extras=['title=\"Joint Distribution\"'])"
    ]
   },
   {
@@ -329,7 +329,7 @@
    ],
    "source": [
     "#| label: vid-event-prob\n",
-    "YouTubeVideo(\"ePADXFKYmh4\")"
+    "YouTubeVideo(\"ePADXFKYmh4\", extras=['title=\"Events and Probabilities\"'])"
    ]
   },
   {
diff --git a/content/Chapter_04/03_Marginal_Distributions.ipynb b/content/Chapter_04/03_Marginal_Distributions.ipynb
index 4a558e50..a514d0d9 100644
--- a/content/Chapter_04/03_Marginal_Distributions.ipynb
+++ b/content/Chapter_04/03_Marginal_Distributions.ipynb
@@ -106,7 +106,7 @@
    "source": [
     "#| label: vid-marginal-dist\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"E-m1o0bxFzo\")"
+    "YouTubeVideo(\"E-m1o0bxFzo\", extras=['title=\"Marginal Distribution\"'])"
    ]
   },
   {
diff --git a/content/Chapter_04/04_Conditional_Distributions.ipynb b/content/Chapter_04/04_Conditional_Distributions.ipynb
index 1d49c994..ca1800d3 100644
--- a/content/Chapter_04/04_Conditional_Distributions.ipynb
+++ b/content/Chapter_04/04_Conditional_Distributions.ipynb
@@ -104,7 +104,7 @@
    "source": [
     "#| label: vid-cd-ex\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"CnFSQPrm8Js\")"
+    "YouTubeVideo(\"CnFSQPrm8Js\", extras=['title=\"Conditional Distribution: Example\"'])"
    ]
   },
   {
@@ -569,7 +569,7 @@
    "source": [
     "#| label: vid-cd-notation-calc\n",
     "from IPython.display import YouTubeVideo\n",
-    "vid_cd_notation_calc = YouTubeVideo(\"8Ihsh1B0Eg4\")"
+    "vid_cd_notation_calc = YouTubeVideo(\"8Ihsh1B0Eg4\", extras=['title=\"Conditional Distribution: Notation and Calculation\"'])"
    ]
   },
   {
diff --git a/content/Chapter_04/05_Dependence_and_Independence.ipynb b/content/Chapter_04/05_Dependence_and_Independence.ipynb
index a33537f0..0c8e7cd1 100644
--- a/content/Chapter_04/05_Dependence_and_Independence.ipynb
+++ b/content/Chapter_04/05_Dependence_and_Independence.ipynb
@@ -820,7 +820,7 @@
    "source": [
     "#| label: vid-ind-def\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"zjKrjQJzqQ4\")"
+    "YouTubeVideo(\"zjKrjQJzqQ4\", extras=['title=\"Independence: Formal Definition\"'])"
    ]
   },
   {
diff --git a/content/Chapter_05/01_Bounding_the_Chance_of_a_Union.ipynb b/content/Chapter_05/01_Bounding_the_Chance_of_a_Union.ipynb
index a47f1333..33d3f184 100644
--- a/content/Chapter_05/01_Bounding_the_Chance_of_a_Union.ipynb
+++ b/content/Chapter_05/01_Bounding_the_Chance_of_a_Union.ipynb
@@ -176,7 +176,7 @@
    "source": [
     "#| label: vid\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"VqozXmsKzLE\")"
+    "YouTubeVideo(\"VqozXmsKzLE\", extras=['title=\"Bounds on the chance of a union\"'])"
    ]
   },
   {
diff --git a/content/Chapter_05/02_Inclusion_Exclusion.ipynb b/content/Chapter_05/02_Inclusion_Exclusion.ipynb
index 8385e027..8878c609 100644
--- a/content/Chapter_05/02_Inclusion_Exclusion.ipynb
+++ b/content/Chapter_05/02_Inclusion_Exclusion.ipynb
@@ -63,7 +63,7 @@
    "source": [
     "#| label: vid-ie-3e\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"HdLtwQ1R4m8\")"
+    "YouTubeVideo(\"HdLtwQ1R4m8\", extras=['title=\"Inclusion-Exclusion: Three Events\"'])"
    ]
   },
   {
@@ -126,7 +126,7 @@
    ],
    "source": [
     "#| label: vid-ie-formula\n",
-    "YouTubeVideo(\"Y5CeWFc3l-c\")"
+    "YouTubeVideo(\"Y5CeWFc3l-c\", extras=['title=\"Inclusion-Exclusion Formula\"'])"
    ]
   },
   {
@@ -216,7 +216,7 @@
    ],
    "source": [
     "#| label: vid-num-terms\n",
-    "YouTubeVideo(\"vz3BPaI0GHQ\")"
+    "YouTubeVideo(\"vz3BPaI0GHQ\", extras=['title=\"Number of Terms\"'])"
    ]
   },
   {
diff --git a/content/Chapter_05/03_The_Matching_Problem.ipynb b/content/Chapter_05/03_The_Matching_Problem.ipynb
index de5263fc..ec3321f2 100644
--- a/content/Chapter_05/03_The_Matching_Problem.ipynb
+++ b/content/Chapter_05/03_The_Matching_Problem.ipynb
@@ -71,7 +71,7 @@
    "source": [
     "#| label: vid-fix-loc\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"wM-DD_j2npE\")"
+    "YouTubeVideo(\"wM-DD_j2npE\", extras=['title=\"Fixed Locations\"'])"
    ]
   },
   {
@@ -164,7 +164,7 @@
    ],
    "source": [
     "#| label: vid-derangement\n",
-    "YouTubeVideo(\"PYp39ZsLxOg\")"
+    "YouTubeVideo(\"PYp39ZsLxOg\", extras=['title=\"Derangement\"'])"
    ]
   },
   {
diff --git a/content/Chapter_05/04_Sampling_Without_Replacement.ipynb b/content/Chapter_05/04_Sampling_Without_Replacement.ipynb
index a707e621..555fdbbf 100644
--- a/content/Chapter_05/04_Sampling_Without_Replacement.ipynb
+++ b/content/Chapter_05/04_Sampling_Without_Replacement.ipynb
@@ -308,7 +308,7 @@
     "#| label: vid-count-cat\n",
     "#| echo: false\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"bKtovlFnwxY\")\n"
+    "YouTubeVideo(\"bKtovlFnwxY\", extras=['title=\"Counting Categories\"'])\n"
    ]
   },
   {
diff --git a/content/Chapter_06/01_Binomial_Distribution.ipynb b/content/Chapter_06/01_Binomial_Distribution.ipynb
index 5279090b..288680b7 100644
--- a/content/Chapter_06/01_Binomial_Distribution.ipynb
+++ b/content/Chapter_06/01_Binomial_Distribution.ipynb
@@ -130,7 +130,7 @@
    "source": [
     "#| label: vid-binom-dist\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"K2Rsnx8YbJQ\")"
+    "YouTubeVideo(\"K2Rsnx8YbJQ\", extras=['title=\"Binomial Distribution\"'])"
    ]
   },
   {
@@ -369,7 +369,7 @@
    ],
    "source": [
     "#| label: vid-cdf\n",
-    "YouTubeVideo('MoBXF8icYkM')"
+    "YouTubeVideo('MoBXF8icYkM', extras=['title=\"CDF\"'])"
    ]
   },
   {
diff --git a/content/Chapter_06/05_Odds_Ratios.ipynb b/content/Chapter_06/05_Odds_Ratios.ipynb
index 0c5a3a46..3e10e55b 100644
--- a/content/Chapter_06/05_Odds_Ratios.ipynb
+++ b/content/Chapter_06/05_Odds_Ratios.ipynb
@@ -79,7 +79,7 @@
    "source": [
     "#| label: vid-consec-odds\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"-5suvosCTCE\")"
+    "YouTubeVideo(\"-5suvosCTCE\", extras=['title=\"Consecutive Odds\"'])"
    ]
   },
   {
@@ -160,7 +160,7 @@
    ],
    "source": [
     "#| label: vid-binom-co\n",
-    "YouTubeVideo(\"F8lx8OmLsXE\")"
+    "YouTubeVideo(\"F8lx8OmLsXE\", extras=['title=\"Binomial Consecutive Odds\"'])"
    ]
   },
   {
@@ -280,7 +280,7 @@
    ],
    "source": [
     "#| label: vid-odds-mode\n",
-    "YouTubeVideo(\"--V_Z2QBc4w\")"
+    "YouTubeVideo(\"--V_Z2QBc4w\", extras=['title=\"Odds and the Mode\"'])"
    ]
   },
   {
diff --git a/content/Chapter_06/06_Law_of_Small_Numbers.ipynb b/content/Chapter_06/06_Law_of_Small_Numbers.ipynb
index cfb8040b..b2454c12 100644
--- a/content/Chapter_06/06_Law_of_Small_Numbers.ipynb
+++ b/content/Chapter_06/06_Law_of_Small_Numbers.ipynb
@@ -152,7 +152,7 @@
    "source": [
     "#| label: vid-recur-form\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"TmKepZHdOg0\")"
+    "YouTubeVideo(\"TmKepZHdOg0\", extras=['title=\"Recursive Formula\"'])"
    ]
   },
   {
@@ -237,7 +237,7 @@
    ],
    "source": [
     "#| label: vid-poi-approx\n",
-    "YouTubeVideo(\"5w1SnSOSTOo\")"
+    "YouTubeVideo(\"5w1SnSOSTOo\", extras=['title=\"Poisson Approximation\"'])"
    ]
   },
   {
diff --git a/content/Chapter_07/01_Poisson_Distribution.ipynb b/content/Chapter_07/01_Poisson_Distribution.ipynb
index efc60cde..41477b87 100644
--- a/content/Chapter_07/01_Poisson_Distribution.ipynb
+++ b/content/Chapter_07/01_Poisson_Distribution.ipynb
@@ -109,7 +109,7 @@
    "source": [
     "#| label: vid-poi-dist\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('mc9r6cgXaHY')"
+    "YouTubeVideo('mc9r6cgXaHY', extras=['title=\"Poisson Distribution\"'])"
    ]
   },
   {
@@ -278,7 +278,7 @@
    ],
    "source": [
     "#| label: vid-sum-poi\n",
-    "YouTubeVideo('BJ-hAV1TiHk')"
+    "YouTubeVideo('BJ-hAV1TiHk', extras=['title=\"Sum of Independent Poissons\"'])"
    ]
   },
   {
diff --git a/content/Chapter_07/02_Poissonizing_the_Binomial.ipynb b/content/Chapter_07/02_Poissonizing_the_Binomial.ipynb
index 4fae6d33..05f06a9b 100644
--- a/content/Chapter_07/02_Poissonizing_the_Binomial.ipynb
+++ b/content/Chapter_07/02_Poissonizing_the_Binomial.ipynb
@@ -87,7 +87,7 @@
    "source": [
     "#| label: vid-poi-trial\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('lnLUsr24r88')"
+    "YouTubeVideo('lnLUsr24r88', extras=['title=\"Poisson Number of Trials\"'])"
    ]
   },
   {
@@ -154,7 +154,7 @@
    ],
    "source": [
     "#| label: vid-poi-success\n",
-    "YouTubeVideo('JQT1NQNbxsQ')"
+    "YouTubeVideo('JQT1NQNbxsQ', extras=['title=\"Poisson Number of Successes\"'])"
    ]
   },
   {
@@ -263,7 +263,7 @@
    ],
    "source": [
     "#| label: vid-success-fail\n",
-    "YouTubeVideo('2qLF6nNWPiw')"
+    "YouTubeVideo('2qLF6nNWPiw', extras=['title=\"Successes and Failures\"'])"
    ]
   },
   {
diff --git a/content/Chapter_08/01_Definition.ipynb b/content/Chapter_08/01_Definition.ipynb
index 9ebb8344..017b9b37 100644
--- a/content/Chapter_08/01_Definition.ipynb
+++ b/content/Chapter_08/01_Definition.ipynb
@@ -74,7 +74,7 @@
    "source": [
     "#| label: vid-eq-def\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('jhKL5nVe0Mk')"
+    "YouTubeVideo('jhKL5nVe0Mk', extras=['title=\"Two Equivalent Definitions\"'])"
    ]
   },
   {
@@ -392,7 +392,7 @@
    ],
    "source": [
     "#| label: vid-exist\n",
-    "YouTubeVideo('9gjBI8SMQis')"
+    "YouTubeVideo('9gjBI8SMQis', extras=['title=\"Existence\"'])"
    ]
   },
   {
diff --git a/content/Chapter_08/02_Applying_the_Definition.ipynb b/content/Chapter_08/02_Applying_the_Definition.ipynb
index c3b5f39f..33f0d3a8 100644
--- a/content/Chapter_08/02_Applying_the_Definition.ipynb
+++ b/content/Chapter_08/02_Applying_the_Definition.ipynb
@@ -235,7 +235,7 @@
    "source": [
     "#| label: vid-exp-poi\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('hq9GiEQ1ZzY')"
+    "YouTubeVideo('hq9GiEQ1ZzY', extras=['title=\"Expectation: Poisson\"'])"
    ]
   },
   {
@@ -355,7 +355,7 @@
    ],
    "source": [
     "#| label: vid-tail-sum\n",
-    "YouTubeVideo('ctcoolPNZjE')"
+    "YouTubeVideo('ctcoolPNZjE', extras=['title=\"Tail Sum Formula\"'])"
    ]
   },
   {
@@ -467,7 +467,7 @@
    ],
    "source": [
     "#| label: vid-exp-geom\n",
-    "YouTubeVideo('f2HhYnaGL2U')"
+    "YouTubeVideo('f2HhYnaGL2U', extras=['title=\"Expectation: Geometric\"'])"
    ]
   },
   {
diff --git a/content/Chapter_08/03_Expectations_of_Functions.ipynb b/content/Chapter_08/03_Expectations_of_Functions.ipynb
index c0f74b6a..c9764b50 100644
--- a/content/Chapter_08/03_Expectations_of_Functions.ipynb
+++ b/content/Chapter_08/03_Expectations_of_Functions.ipynb
@@ -151,7 +151,7 @@
    "source": [
     "#| label: vid-nonlinear-obs\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('BWNa1Ri7eII')"
+    "YouTubeVideo('BWNa1Ri7eII', extras=['title=\"Non-linear Function: Observation\"'])"
    ]
   },
   {
@@ -240,7 +240,7 @@
    ],
    "source": [
     "#| label: vid-nonlinear-calc\n",
-    "YouTubeVideo('jVBrCMCzO3o')"
+    "YouTubeVideo('jVBrCMCzO3o', extras=['title=\"Non-linear Function: Calculation\"'])"
    ]
   },
   {
diff --git a/content/Chapter_08/04_Additivity.ipynb b/content/Chapter_08/04_Additivity.ipynb
index 887e4a12..5d8e1ffd 100644
--- a/content/Chapter_08/04_Additivity.ipynb
+++ b/content/Chapter_08/04_Additivity.ipynb
@@ -120,7 +120,7 @@
    "source": [
     "#| label: vid-add-exp\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('HzZEhM4NHUQ')"
+    "YouTubeVideo('HzZEhM4NHUQ', extras=['title=\"Additivity of Expectation\"'])"
    ]
   },
   {
@@ -346,7 +346,7 @@
    ],
    "source": [
     "#| label: vid-ex-ue\n",
-    "YouTubeVideo('ruEpGZJwHmw')"
+    "YouTubeVideo('ruEpGZJwHmw', extras=['title=\"Example of an Unbiased Estimator\"'])"
    ]
   },
   {
diff --git a/content/Chapter_08/05_Method_of_Indicators.ipynb b/content/Chapter_08/05_Method_of_Indicators.ipynb
index ab9ce015..87e5a0c8 100644
--- a/content/Chapter_08/05_Method_of_Indicators.ipynb
+++ b/content/Chapter_08/05_Method_of_Indicators.ipynb
@@ -105,7 +105,7 @@
    "source": [
     "#| label: vid-method-ind\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('hOIcQUYUNsM')"
+    "YouTubeVideo('hOIcQUYUNsM', extras=['title=\"Method of Indicators\"'])"
    ]
   },
   {
@@ -207,7 +207,7 @@
    ],
    "source": [
     "#| label: vid-exp-binom-hyp\n",
-    "YouTubeVideo('lz-UuQqvUOE')"
+    "YouTubeVideo('lz-UuQqvUOE', extras=['title=\"Expectation: Binomial and Hypergeometric\"'])"
    ]
   },
   {
@@ -362,7 +362,7 @@
    ],
    "source": [
     "#| label: vid-apply-moi\n",
-    "YouTubeVideo('mxj4Gr_QUCM')"
+    "YouTubeVideo('mxj4Gr_QUCM', extras=['title=\"Applying the Method of Indicators\"'])"
    ]
   },
   {
diff --git a/content/Chapter_09/01_Probability_by_Conditioning.ipynb b/content/Chapter_09/01_Probability_by_Conditioning.ipynb
index 61e1016d..d8323887 100644
--- a/content/Chapter_09/01_Probability_by_Conditioning.ipynb
+++ b/content/Chapter_09/01_Probability_by_Conditioning.ipynb
@@ -82,7 +82,7 @@
    "source": [
     "#| label: vid-cond-em\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('BCx-DlphihU')"
+    "YouTubeVideo('BCx-DlphihU', extras=['title=\"Conditioning on Early Moves\"'])"
    ]
   },
   {
diff --git a/content/Chapter_09/02_Expectation_by_Conditioning.ipynb b/content/Chapter_09/02_Expectation_by_Conditioning.ipynb
index 942cbe92..8ebffe99 100644
--- a/content/Chapter_09/02_Expectation_by_Conditioning.ipynb
+++ b/content/Chapter_09/02_Expectation_by_Conditioning.ipynb
@@ -82,7 +82,7 @@
    "source": [
     "#| label: vid-exp-by-cond\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('GrM0Ve-a010')"
+    "YouTubeVideo('GrM0Ve-a010', extras=['title=\"Expectation by Conditioning\"'])"
    ]
   },
   {
@@ -560,7 +560,7 @@
    "source": [
     "#| label: vid-cond-exp\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('k0CaLXwRCz0')"
+    "YouTubeVideo('k0CaLXwRCz0', extras=['title=\"Conditional Expectation\"'])"
    ]
   },
   {
diff --git a/content/Chapter_09/03_Expected_Waiting_Times.ipynb b/content/Chapter_09/03_Expected_Waiting_Times.ipynb
index 3938e187..3bb337f0 100644
--- a/content/Chapter_09/03_Expected_Waiting_Times.ipynb
+++ b/content/Chapter_09/03_Expected_Waiting_Times.ipynb
@@ -75,7 +75,7 @@
    "source": [
     "#| label: vid-exp-geom-rev\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('MxeioidxzTA')"
+    "YouTubeVideo('MxeioidxzTA', extras=['title=\"Expectation of the Geometric, Revisited\"'])"
    ]
   },
   {
@@ -225,7 +225,7 @@
    ],
    "source": [
     "#| label: vid-tree-ans2\n",
-    "YouTubeVideo('XivRI0ba2v8')"
+    "YouTubeVideo('XivRI0ba2v8', extras=['title=\"Tree diagram for Answer 2\"'])"
    ]
   },
   {
diff --git a/content/Chapter_10/01_Transitions.ipynb b/content/Chapter_10/01_Transitions.ipynb
index 0d967949..2fc1ba55 100644
--- a/content/Chapter_10/01_Transitions.ipynb
+++ b/content/Chapter_10/01_Transitions.ipynb
@@ -85,7 +85,7 @@
    "source": [
     "#| label: vid-markov-prop\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('YVGKlWO9ANk')"
+    "YouTubeVideo('YVGKlWO9ANk', extras=['title=\"Markov Property\"'])"
    ]
   },
   {
@@ -217,7 +217,7 @@
    ],
    "source": [
     "#| label: vid-transactions\n",
-    "YouTubeVideo('G_g-45W7yYk')"
+    "YouTubeVideo('G_g-45W7yYk', extras=['title=\"Transitions\"'])"
    ]
   },
   {
@@ -622,7 +622,7 @@
    ],
    "source": [
     "#| label: vid-nstep-tm\n",
-    "YouTubeVideo('YQziLVkV_Ro')"
+    "YouTubeVideo('YQziLVkV_Ro', extras=['title=\"n-Step Transition Matrix\"'])"
    ]
   },
   {
diff --git a/content/Chapter_10/02_Deconstructing_Chains.ipynb b/content/Chapter_10/02_Deconstructing_Chains.ipynb
index 2371b6e0..ca2833af 100644
--- a/content/Chapter_10/02_Deconstructing_Chains.ipynb
+++ b/content/Chapter_10/02_Deconstructing_Chains.ipynb
@@ -59,7 +59,7 @@
    "source": [
     "#| label: vid-irre\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('Cnosegp3IlI')"
+    "YouTubeVideo('Cnosegp3IlI', extras=['title=\"Irreducibility\"'])"
    ]
   },
   {
@@ -132,7 +132,7 @@
    ],
    "source": [
     "#| label: vid-aperiod\n",
-    "YouTubeVideo('dtsukvJUscs')"
+    "YouTubeVideo('dtsukvJUscs', extras=['title=\"Aperiodicity\"'])"
    ]
   },
   {
diff --git a/content/Chapter_10/03_Long_Run_Behavior.ipynb b/content/Chapter_10/03_Long_Run_Behavior.ipynb
index e49d2f95..c171b32a 100644
--- a/content/Chapter_10/03_Long_Run_Behavior.ipynb
+++ b/content/Chapter_10/03_Long_Run_Behavior.ipynb
@@ -122,7 +122,7 @@
    "source": [
     "#| label: vid-big-thm\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('5ulR-5VcgLQ')"
+    "YouTubeVideo('5ulR-5VcgLQ', extras=['title=\"The Big Theorem\"'])"
    ]
   },
   {
@@ -204,7 +204,7 @@
    ],
    "source": [
     "#| label: vid-balance-eq\n",
-    "YouTubeVideo('Qxg5EAP90dw')"
+    "YouTubeVideo('Qxg5EAP90dw', extras=['title=\"Balance Equations\"'])"
    ]
   },
   {
@@ -540,7 +540,7 @@
    ],
    "source": [
     "#| label: vid-steady-state\n",
-    "YouTubeVideo('LIXffVd1wic')"
+    "YouTubeVideo('LIXffVd1wic', extras=['title=\"Steady State\"'])"
    ]
   },
   {
diff --git a/content/Chapter_11/01_Balance_and_Detailed_Balance.ipynb b/content/Chapter_11/01_Balance_and_Detailed_Balance.ipynb
index 443898ae..9d81f419 100644
--- a/content/Chapter_11/01_Balance_and_Detailed_Balance.ipynb
+++ b/content/Chapter_11/01_Balance_and_Detailed_Balance.ipynb
@@ -61,7 +61,7 @@
    "source": [
     "#| label: vid-bal\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('SfH-oJ-PTC4')"
+    "YouTubeVideo('SfH-oJ-PTC4', extras=['title=\"Balance\"'])"
    ]
   },
   {
@@ -153,7 +153,7 @@
    ],
    "source": [
     "#| label: vid-detailed-balance\n",
-    "YouTubeVideo('KEOTepEQCrU')"
+    "YouTubeVideo('KEOTepEQCrU', extras=['title=\"Detailed Balance\"'])"
    ]
   },
   {
@@ -231,7 +231,7 @@
    ],
    "source": [
     "#| label: vid-bod-chain\n",
-    "YouTubeVideo('CZNtQV5JpsU')"
+    "YouTubeVideo('CZNtQV5JpsU', extras=['title=\"Birth-and-Death Chains\"'])"
    ]
   },
   {
diff --git a/content/Chapter_11/02_Code_Breaking.ipynb b/content/Chapter_11/02_Code_Breaking.ipynb
index 3cc42d55..4898bc86 100644
--- a/content/Chapter_11/02_Code_Breaking.ipynb
+++ b/content/Chapter_11/02_Code_Breaking.ipynb
@@ -175,7 +175,7 @@
    "source": [
     "#| label: vid-assum-nc\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('C18hs5sHf0Y')"
+    "YouTubeVideo('C18hs5sHf0Y', extras=['title=\"Assumptions\"'])"
    ]
   },
   {
@@ -236,7 +236,7 @@
    ],
    "source": [
     "#| label: vid-decoders\n",
-    "YouTubeVideo('MUdZWq3b0mg')"
+    "YouTubeVideo('MUdZWq3b0mg', extras=['title=\"Decoders\"'])"
    ]
   },
   {
@@ -366,7 +366,7 @@
    ],
    "source": [
     "#| label: vid-bigram\n",
-    "YouTubeVideo('OrLjMVjpryY')"
+    "YouTubeVideo('OrLjMVjpryY', extras=['title=\"Bigrams\"'])"
    ]
   },
   {
@@ -492,7 +492,7 @@
    ],
    "source": [
     "#| label: vid-score-decode\n",
-    "YouTubeVideo('pi36R1lN8ww')"
+    "YouTubeVideo('pi36R1lN8ww', extras=['title=\"Score of a Decoder\"'])"
    ]
   },
   {
@@ -598,7 +598,7 @@
    ],
    "source": [
     "#| label: vid-best-decode\n",
-    "YouTubeVideo('zCAeQUaPf1E')"
+    "YouTubeVideo('zCAeQUaPf1E', extras=['title=\"The Best Decoder\"'])"
    ]
   },
   {
@@ -729,7 +729,7 @@
    ],
    "source": [
     "#| label: vid-larger-aplh\n",
-    "YouTubeVideo('XLd2MuMIlR4')"
+    "YouTubeVideo('XLd2MuMIlR4', extras=['title=\"Larger Alphabet\"'])"
    ]
   },
   {
diff --git a/content/Chapter_11/03_Metropolis_Algorithm.ipynb b/content/Chapter_11/03_Metropolis_Algorithm.ipynb
index a9ab3efb..3825fe1f 100644
--- a/content/Chapter_11/03_Metropolis_Algorithm.ipynb
+++ b/content/Chapter_11/03_Metropolis_Algorithm.ipynb
@@ -59,7 +59,7 @@
    "source": [
     "#| label: vid-metro-setup\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('LwkIn66bkXc')"
+    "YouTubeVideo('LwkIn66bkXc', extras=['title=\"Metropolis: Setup\"'])"
    ]
   },
   {
@@ -170,7 +170,7 @@
    ],
    "source": [
     "#| label: vid-metro-algo\n",
-    "YouTubeVideo('0_JsYu1NpBo')"
+    "YouTubeVideo('0_JsYu1NpBo', extras=['title=\"Metropolis Algorithm\"'])"
    ]
   },
   {
diff --git a/content/Chapter_12/01_Definition.ipynb b/content/Chapter_12/01_Definition.ipynb
index 1b4ade46..613962c9 100644
--- a/content/Chapter_12/01_Definition.ipynb
+++ b/content/Chapter_12/01_Definition.ipynb
@@ -92,7 +92,7 @@
    "source": [
     "#| label: vid-sd\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('9h34zgXmuqo')"
+    "YouTubeVideo('9h34zgXmuqo', extras=['title=\"Standard Deviation\"'])"
    ]
   },
   {
@@ -466,7 +466,7 @@
    ],
    "source": [
     "#| label: vid-linear-func\n",
-    "YouTubeVideo('zF-_2JwObjQ')"
+    "YouTubeVideo('zF-_2JwObjQ', extras=['title=\"SD: Linear Functions\"'])"
    ]
   },
   {
@@ -612,7 +612,7 @@
    ],
    "source": [
     "#| label: vid-var-ind\n",
-    "YouTubeVideo('lfJWb_s3MSI')"
+    "YouTubeVideo('lfJWb_s3MSI', extras=['title=\"Variance of Indicator\"'])"
    ]
   },
   {
diff --git a/content/Chapter_12/02_Prediction_and_Estimation.ipynb b/content/Chapter_12/02_Prediction_and_Estimation.ipynb
index 69968354..bfb291f6 100644
--- a/content/Chapter_12/02_Prediction_and_Estimation.ipynb
+++ b/content/Chapter_12/02_Prediction_and_Estimation.ipynb
@@ -93,7 +93,7 @@
    "source": [
     "#| label: vid-ls-cons-pred\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('hnm1Ht5DiWk')"
+    "YouTubeVideo('hnm1Ht5DiWk', extras=['title=\"Least Squares Constant Predictor\"'])"
    ]
   },
   {
diff --git a/content/Chapter_12/03_Bounds.ipynb b/content/Chapter_12/03_Bounds.ipynb
index 021b3969..608a1065 100644
--- a/content/Chapter_12/03_Bounds.ipynb
+++ b/content/Chapter_12/03_Bounds.ipynb
@@ -174,7 +174,7 @@
    "source": [
     "#| label: vid-markov-ineq\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('WIokPScne_8')"
+    "YouTubeVideo('WIokPScne_8', extras=['title=\"Markov's Inequality\"'])"
    ]
   },
   {
@@ -366,7 +366,7 @@
    ],
    "source": [
     "#| label: vid-chebyshev-ineq\n",
-    "YouTubeVideo('n6DilL4PzAQ')"
+    "YouTubeVideo('n6DilL4PzAQ', extras=['title=\"Chebyshev's Inequality\"'])"
    ]
   },
   {
@@ -548,7 +548,7 @@
    ],
    "source": [
     "#| label: vid-std-unit\n",
-    "YouTubeVideo('y6RF-WBJciY')"
+    "YouTubeVideo('y6RF-WBJciY', extras=['title=\"Standard Units\"'])"
    ]
   },
   {
diff --git a/content/Chapter_13/01_Covariance.ipynb b/content/Chapter_13/01_Covariance.ipynb
index fac95078..5079c5b5 100644
--- a/content/Chapter_13/01_Covariance.ipynb
+++ b/content/Chapter_13/01_Covariance.ipynb
@@ -88,7 +88,7 @@
    "source": [
     "#| label: vid-cov-def\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('0yvoSUYo2GY')"
+    "YouTubeVideo('0yvoSUYo2GY', extras=['title=\"Covariance: Definition\"'])"
    ]
   },
   {
@@ -236,7 +236,7 @@
    ],
    "source": [
     "#| label: vid-cov-main-use\n",
-    "YouTubeVideo('yJ580tGWdGg')"
+    "YouTubeVideo('yJ580tGWdGg', extras=['title=\"Covariance: Main Uses\"'])"
    ]
   },
   {
diff --git a/content/Chapter_13/02_Properties_of_Covariance.ipynb b/content/Chapter_13/02_Properties_of_Covariance.ipynb
index 49689a66..01adf781 100644
--- a/content/Chapter_13/02_Properties_of_Covariance.ipynb
+++ b/content/Chapter_13/02_Properties_of_Covariance.ipynb
@@ -150,7 +150,7 @@
    "source": [
     "#| label: vid-uncorr-not-indep\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('_FG4CxA6_9A')"
+    "YouTubeVideo('_FG4CxA6_9A', extras=['title=\"Uncorrelated but Not Independent\"'])"
    ]
   },
   {
@@ -245,7 +245,7 @@
    ],
    "source": [
     "#| label: vid-indep-imp-uncorr\n",
-    "YouTubeVideo('y8GIvXI7b-s')"
+    "YouTubeVideo('y8GIvXI7b-s', extras=['title=\"Independent Implies Uncorrelated\"'])"
    ]
   },
   {
@@ -325,7 +325,7 @@
    ],
    "source": [
     "#| label: vid-bilinear-cov\n",
-    "YouTubeVideo('Fs-rX3jdErU')"
+    "YouTubeVideo('Fs-rX3jdErU', extras=['title=\"Bilinearity of Covariance\"'])"
    ]
   },
   {
diff --git a/content/Chapter_13/03_Sums_of_Independent_Variables.ipynb b/content/Chapter_13/03_Sums_of_Independent_Variables.ipynb
index 67692c72..96ef20fe 100644
--- a/content/Chapter_13/03_Sums_of_Independent_Variables.ipynb
+++ b/content/Chapter_13/03_Sums_of_Independent_Variables.ipynb
@@ -81,7 +81,7 @@
    "source": [
     "#| label: vid-var-sum\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('M3Y_oBn2o7Y')"
+    "YouTubeVideo('M3Y_oBn2o7Y', extras=['title=\"Variance of a Sum\"'])"
    ]
   },
   {
@@ -179,7 +179,7 @@
    ],
    "source": [
     "#| label: vid-var-iid-sum\n",
-    "YouTubeVideo('jHcdiO5N0ng')"
+    "YouTubeVideo('jHcdiO5N0ng', extras=['title=\"Variance of IID Sample Sum\"'])"
    ]
   },
   {
@@ -347,7 +347,7 @@
    ],
    "source": [
     "#| label: vid-binom-poi-var\n",
-    "YouTubeVideo('KoWHPWJBeJo')"
+    "YouTubeVideo('KoWHPWJBeJo', extras=['title=\"Binomial and Poisson Variance\"'])"
    ]
   },
   {
diff --git a/content/Chapter_13/04_Symmetry_and_Indicators.ipynb b/content/Chapter_13/04_Symmetry_and_Indicators.ipynb
index 8dc34d6c..fa22b58b 100644
--- a/content/Chapter_13/04_Symmetry_and_Indicators.ipynb
+++ b/content/Chapter_13/04_Symmetry_and_Indicators.ipynb
@@ -108,7 +108,7 @@
    "source": [
     "#| label: vid-cov-ind2\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('9j8VwhEsWrk')"
+    "YouTubeVideo('9j8VwhEsWrk', extras=['title=\"Covariance of Two Indicators\"'])"
    ]
   },
   {
@@ -245,7 +245,7 @@
    ],
    "source": [
     "#| label: vid-vars-srs-sum\n",
-    "YouTubeVideo('XWHrPZOgD0A')"
+    "YouTubeVideo('XWHrPZOgD0A', extras=['title=\"Variance of a Simple Random Sample Sum\"'])"
    ]
   },
   {
@@ -340,7 +340,7 @@
    ],
    "source": [
     "#| label: vid-var-hyp\n",
-    "YouTubeVideo('SJ2X0h2XdiY')"
+    "YouTubeVideo('SJ2X0h2XdiY', extras=['title=\"Variance of the Hypergeometric\"'])"
    ]
   },
   {
diff --git a/content/Chapter_14/01_Exact_Distribution_of_a_Sum.ipynb b/content/Chapter_14/01_Exact_Distribution_of_a_Sum.ipynb
index 4b08ed49..25391d93 100644
--- a/content/Chapter_14/01_Exact_Distribution_of_a_Sum.ipynb
+++ b/content/Chapter_14/01_Exact_Distribution_of_a_Sum.ipynb
@@ -96,7 +96,7 @@
    "source": [
     "#| label: vid-pgf\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('Z1TU6n3ZdSg')"
+    "YouTubeVideo('Z1TU6n3ZdSg', extras=['title=\"Probability Generating Function\"'])"
    ]
   },
   {
@@ -197,7 +197,7 @@
    ],
    "source": [
     "#| label: vid-pgf-sum\n",
-    "YouTubeVideo('86C9pL8lg1U')"
+    "YouTubeVideo('86C9pL8lg1U', extras=['title=\"PGF of a Sum\"'])"
    ]
   },
   {
diff --git a/content/Chapter_14/02_PGFs_in_NumPy.ipynb b/content/Chapter_14/02_PGFs_in_NumPy.ipynb
index 3857b50f..6967d620 100644
--- a/content/Chapter_14/02_PGFs_in_NumPy.ipynb
+++ b/content/Chapter_14/02_PGFs_in_NumPy.ipynb
@@ -87,7 +87,7 @@
    "source": [
     "#| label: vid-pgf-numpy\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('e3ItAqkAhj4')"
+    "YouTubeVideo('e3ItAqkAhj4', extras=['title=\"PGF in NumPy\"'])"
    ]
   },
   {
@@ -244,7 +244,7 @@
    ],
    "source": [
     "#| label: vid-pgf-sum-iid\n",
-    "YouTubeVideo('dJCAW5CRJlc')"
+    "YouTubeVideo('dJCAW5CRJlc', extras=['title=\"PGF of a Sum of IID Random Variables\"'])"
    ]
   },
   {
diff --git a/content/Chapter_14/03_Central_Limit_Theorem.ipynb b/content/Chapter_14/03_Central_Limit_Theorem.ipynb
index aeded676..9166d04c 100644
--- a/content/Chapter_14/03_Central_Limit_Theorem.ipynb
+++ b/content/Chapter_14/03_Central_Limit_Theorem.ipynb
@@ -217,7 +217,7 @@
    "source": [
     "#| label: vid-clt\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('xf7-Cy4JWjE')"
+    "YouTubeVideo('xf7-Cy4JWjE', extras=['title=\"Central Limit Theorem\"'])"
    ]
   },
   {
@@ -367,7 +367,7 @@
    ],
    "source": [
     "#| label: vid-prob-std-normal\n",
-    "YouTubeVideo('ufuTZYneoJc')"
+    "YouTubeVideo('ufuTZYneoJc', extras=['title=\"Probabilities and the Standard Normal Curve\"'])"
    ]
   },
   {
diff --git a/content/Chapter_14/05_The_Sample_Mean.ipynb b/content/Chapter_14/05_The_Sample_Mean.ipynb
index f6162e53..dbc0000d 100644
--- a/content/Chapter_14/05_The_Sample_Mean.ipynb
+++ b/content/Chapter_14/05_The_Sample_Mean.ipynb
@@ -172,7 +172,7 @@
    "source": [
     "#| label: vid-iid-sample-mean\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('UjxM2o6nlbc')"
+    "YouTubeVideo('UjxM2o6nlbc', extras=['title=\"IID Sample Mean\"'])"
    ]
   },
   {
@@ -293,7 +293,7 @@
    ],
    "source": [
     "#| label: vid-wlln\n",
-    "YouTubeVideo('WowLqZku3X8')"
+    "YouTubeVideo('WowLqZku3X8', extras=['title=\"Weak Law of Large Numbers\"'])"
    ]
   },
   {
diff --git a/content/Chapter_14/06_Confidence_Intervals.ipynb b/content/Chapter_14/06_Confidence_Intervals.ipynb
index 213fe068..2f4b4da0 100644
--- a/content/Chapter_14/06_Confidence_Intervals.ipynb
+++ b/content/Chapter_14/06_Confidence_Intervals.ipynb
@@ -89,7 +89,7 @@
    "source": [
     "#| label: vid-95ci-mean\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('-t8E1SW0wsg')"
+    "YouTubeVideo('-t8E1SW0wsg', extras=['title=\"95% Confidence Interval for Population Mean\"'])"
    ]
   },
   {
@@ -313,7 +313,7 @@
    ],
    "source": [
     "#| label: vid-ci-pop-mean\n",
-    "YouTubeVideo('K7juQV1jezw')"
+    "YouTubeVideo('K7juQV1jezw', extras=['title=\"Confidence Interval for Population Mean\"'])"
    ]
   },
   {
@@ -601,7 +601,7 @@
    ],
    "source": [
     "#| label: vid-est-sd-mean\n",
-    "YouTubeVideo('iPnAPZQgjkA')"
+    "YouTubeVideo('iPnAPZQgjkA', extras=['title=\"Estimating the SD of the Sample Mean\"'])"
    ]
   },
   {
diff --git a/content/Chapter_15/01_Density_and_CDF.ipynb b/content/Chapter_15/01_Density_and_CDF.ipynb
index 034daff1..31ea9b07 100644
--- a/content/Chapter_15/01_Density_and_CDF.ipynb
+++ b/content/Chapter_15/01_Density_and_CDF.ipynb
@@ -76,7 +76,7 @@
    "source": [
     "#| label: vid-intro-dense\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('YU3ShHl3wE8')"
+    "YouTubeVideo('YU3ShHl3wE8', extras=['title=\"Introduction to Density\"'])"
    ]
   },
   {
@@ -342,7 +342,7 @@
    ],
    "source": [
     "#| label: vid-cdf-intro\n",
-    "YouTubeVideo('heahvMpBU_E')"
+    "YouTubeVideo('heahvMpBU_E', extras=['title=\"CDF\"'])"
    ]
   },
   {
diff --git a/content/Chapter_15/02_The_Meaning_of_Density.ipynb b/content/Chapter_15/02_The_Meaning_of_Density.ipynb
index 3401c6af..926df3ed 100644
--- a/content/Chapter_15/02_The_Meaning_of_Density.ipynb
+++ b/content/Chapter_15/02_The_Meaning_of_Density.ipynb
@@ -129,7 +129,7 @@
    "source": [
     "#| label: vid-meaning-dense\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('7A7MqighkTA')"
+    "YouTubeVideo('7A7MqighkTA', extras=['title=\"Meaning of Density\"'])"
    ]
   },
   {
diff --git a/content/Chapter_15/03_Expectation.ipynb b/content/Chapter_15/03_Expectation.ipynb
index 9d5e05e1..0c742dbd 100644
--- a/content/Chapter_15/03_Expectation.ipynb
+++ b/content/Chapter_15/03_Expectation.ipynb
@@ -77,7 +77,7 @@
    "source": [
     "#| label: vid-def-exp-cont\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('gJkb-5YXly4')"
+    "YouTubeVideo('gJkb-5YXly4', extras=['title=\"Definition of Expectation\"'])"
    ]
   },
   {
diff --git a/content/Chapter_15/04_Exponential_Distribution.ipynb b/content/Chapter_15/04_Exponential_Distribution.ipynb
index a6524c12..0eee5689 100644
--- a/content/Chapter_15/04_Exponential_Distribution.ipynb
+++ b/content/Chapter_15/04_Exponential_Distribution.ipynb
@@ -124,7 +124,7 @@
    "source": [
     "#| label: vid-exp-dist\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('D8DAKgt-NUk')"
+    "YouTubeVideo('D8DAKgt-NUk', extras=['title=\"Exponential Distribution\"'])"
    ]
   },
   {
@@ -422,7 +422,7 @@
    ],
    "source": [
     "#| label: vid-mem-prop\n",
-    "YouTubeVideo('x9ECxtFPrKE')"
+    "YouTubeVideo('x9ECxtFPrKE', extras=['title=\"Memoryless Property\"'])"
    ]
   },
   {
diff --git a/content/Chapter_16/01_Linear_Transformations.ipynb b/content/Chapter_16/01_Linear_Transformations.ipynb
index 769fe0e4..77c805a0 100644
--- a/content/Chapter_16/01_Linear_Transformations.ipynb
+++ b/content/Chapter_16/01_Linear_Transformations.ipynb
@@ -186,7 +186,7 @@
    "source": [
     "#| label: vid_linear_chvar\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('MA40E9QPCjU')"
+    "YouTubeVideo('MA40E9QPCjU', extras=['title=\"Linear Change of Variable\"'])"
    ]
   },
   {
diff --git a/content/Chapter_16/02_Monotone_Functions.ipynb b/content/Chapter_16/02_Monotone_Functions.ipynb
index 3c70c366..8405231d 100644
--- a/content/Chapter_16/02_Monotone_Functions.ipynb
+++ b/content/Chapter_16/02_Monotone_Functions.ipynb
@@ -100,7 +100,7 @@
    "source": [
     "#| label: vid-cov-mono\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('1fKqhkOXddc')"
+    "YouTubeVideo('1fKqhkOXddc', extras=['title=\"Change of Variable  Monotone Function\"'])"
    ]
   },
   {
diff --git a/content/Chapter_17/01_Probabilities_and_Expectations.ipynb b/content/Chapter_17/01_Probabilities_and_Expectations.ipynb
index 8550ce93..4331abc4 100644
--- a/content/Chapter_17/01_Probabilities_and_Expectations.ipynb
+++ b/content/Chapter_17/01_Probabilities_and_Expectations.ipynb
@@ -75,7 +75,7 @@
    "source": [
     "#| label: vid-joint-dense\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('_DzYX2ia_zs')"
+    "YouTubeVideo('_DzYX2ia_zs', extras=['title=\"Joint Density\"'])"
    ]
   },
   {
@@ -171,7 +171,7 @@
    ],
    "source": [
     "#| label: vid-meaning-jd\n",
-    "YouTubeVideo('qfwy23iZBwk')"
+    "YouTubeVideo('qfwy23iZBwk', extras=['title=\"Meaning of Joint Density\"'])"
    ]
   },
   {
@@ -354,7 +354,7 @@
    ],
    "source": [
     "#| label: vid-tot-vol\n",
-    "YouTubeVideo('IP7qhyvy6qE')"
+    "YouTubeVideo('IP7qhyvy6qE', extras=['title=\"Total Volume\"'])"
    ]
   },
   {
@@ -558,7 +558,7 @@
    ],
    "source": [
     "#| label: vid-prob-vol\n",
-    "YouTubeVideo('3Ri-wBDfSMI')"
+    "YouTubeVideo('3Ri-wBDfSMI', extras=['title=\"Probabilities as Volumes\"'])"
    ]
   },
   {
@@ -878,7 +878,7 @@
    ],
    "source": [
     "#| label: vid-exp-joint\n",
-    "YouTubeVideo('iWDyZg5Ckfo')"
+    "YouTubeVideo('iWDyZg5Ckfo', extras=['title=\"Expectation\"'])"
    ]
   },
   {
diff --git a/content/Chapter_17/02_Independence.ipynb b/content/Chapter_17/02_Independence.ipynb
index 91038056..88cac381 100644
--- a/content/Chapter_17/02_Independence.ipynb
+++ b/content/Chapter_17/02_Independence.ipynb
@@ -113,7 +113,7 @@
    "source": [
     "#| label: vid-indep-joint\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('jVHgjZBMYq0')"
+    "YouTubeVideo('jVHgjZBMYq0', extras=['title=\"Independence\"'])"
    ]
   },
   {
diff --git a/content/Chapter_17/03_Marginal_and_Conditional_Densities.ipynb b/content/Chapter_17/03_Marginal_and_Conditional_Densities.ipynb
index a6bc2058..a6fe76c9 100644
--- a/content/Chapter_17/03_Marginal_and_Conditional_Densities.ipynb
+++ b/content/Chapter_17/03_Marginal_and_Conditional_Densities.ipynb
@@ -75,7 +75,7 @@
    "source": [
     "#| label: vid-marg-dense\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('K4NlpU_Kd_0')"
+    "YouTubeVideo('K4NlpU_Kd_0', extras=['title=\"Marginals\"'])"
    ]
   },
   {
@@ -444,7 +444,7 @@
    ],
    "source": [
     "#| label: vid-cond-dense\n",
-    "YouTubeVideo('T0CkUU6V6CE')"
+    "YouTubeVideo('T0CkUU6V6CE', extras=['title=\"Conditioning\"'])"
    ]
   },
   {
diff --git a/content/Chapter_17/04_Beta_Densities_with_Integer_Parameters.ipynb b/content/Chapter_17/04_Beta_Densities_with_Integer_Parameters.ipynb
index 1a0879c2..2ec0a8f3 100644
--- a/content/Chapter_17/04_Beta_Densities_with_Integer_Parameters.ipynb
+++ b/content/Chapter_17/04_Beta_Densities_with_Integer_Parameters.ipynb
@@ -195,7 +195,7 @@
    "source": [
     "#| label: vid-joint-os\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('3AIyXF1kQPA')"
+    "YouTubeVideo('3AIyXF1kQPA', extras=['title=\"Joint Density of Order Statistics\"'])"
    ]
   },
   {
diff --git a/content/Chapter_18/01_Standard_Normal_Basics.ipynb b/content/Chapter_18/01_Standard_Normal_Basics.ipynb
index 24010d87..195aec18 100644
--- a/content/Chapter_18/01_Standard_Normal_Basics.ipynb
+++ b/content/Chapter_18/01_Standard_Normal_Basics.ipynb
@@ -109,7 +109,7 @@
    "source": [
     "#| label: vid-const-int\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('8vLTUrqS67A')"
+    "YouTubeVideo('8vLTUrqS67A', extras=['title=\"Constant of Integration\"'])"
    ]
   },
   {
@@ -345,7 +345,7 @@
    ],
    "source": [
     "#| label: vid-sn-basics\n",
-    "YouTubeVideo('190dwmZKKPk')"
+    "YouTubeVideo('190dwmZKKPk', extras=['title=\"Standard Normal Basics\"'])"
    ]
   },
   {
diff --git a/content/Chapter_18/03_The_Gamma_Family.ipynb b/content/Chapter_18/03_The_Gamma_Family.ipynb
index 49765192..bc95fc93 100644
--- a/content/Chapter_18/03_The_Gamma_Family.ipynb
+++ b/content/Chapter_18/03_The_Gamma_Family.ipynb
@@ -313,7 +313,7 @@
    "source": [
     "#| label: vid-gamma-int-param\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('BQoIs4eWZa4')"
+    "YouTubeVideo('BQoIs4eWZa4', extras=['title=\"Gamma with Integer Shape Parameter\"'])"
    ]
   },
   {
diff --git a/content/Chapter_18/04_Chi_Squared_Distributions.ipynb b/content/Chapter_18/04_Chi_Squared_Distributions.ipynb
index dc6883dc..fb4fe933 100644
--- a/content/Chapter_18/04_Chi_Squared_Distributions.ipynb
+++ b/content/Chapter_18/04_Chi_Squared_Distributions.ipynb
@@ -98,7 +98,7 @@
    "source": [
     "#| label: vid-chi2\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('TXM-yzqYmwg')"
+    "YouTubeVideo('TXM-yzqYmwg', extras=['title=\"Chi-Squared Distributions\"'])"
    ]
   },
   {
diff --git a/content/Chapter_19/02_Moment_Generating_Functions.ipynb b/content/Chapter_19/02_Moment_Generating_Functions.ipynb
index 615c8588..1331f454 100644
--- a/content/Chapter_19/02_Moment_Generating_Functions.ipynb
+++ b/content/Chapter_19/02_Moment_Generating_Functions.ipynb
@@ -103,7 +103,7 @@
    "source": [
     "#| label: vid-mgf\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('uljcfZRj7os')"
+    "YouTubeVideo('uljcfZRj7os', extras=['title=\"Moment Generating Functions\"'])"
    ]
   },
   {
@@ -313,7 +313,7 @@
    ],
    "source": [
     "#| label: vid-mgf-tail\n",
-    "YouTubeVideo('juRbr3EXnJg')"
+    "YouTubeVideo('juRbr3EXnJg', extras=['title=\"MGF and Tails\"'])"
    ]
   },
   {
diff --git a/content/Chapter_19/03_MGFs_Normal_and_the_CLT.ipynb b/content/Chapter_19/03_MGFs_Normal_and_the_CLT.ipynb
index 2af138ab..61714afb 100644
--- a/content/Chapter_19/03_MGFs_Normal_and_the_CLT.ipynb
+++ b/content/Chapter_19/03_MGFs_Normal_and_the_CLT.ipynb
@@ -52,7 +52,7 @@
    "source": [
     "#| label: vid-normal-mgf\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('SXyYS0ztJ6U')"
+    "YouTubeVideo('SXyYS0ztJ6U', extras=['title=\"Normal MGF\"'])"
    ]
   },
   {
diff --git a/content/Chapter_19/04_Chernoff_Bound.ipynb b/content/Chapter_19/04_Chernoff_Bound.ipynb
index 16a8cb47..06300e6f 100644
--- a/content/Chapter_19/04_Chernoff_Bound.ipynb
+++ b/content/Chapter_19/04_Chernoff_Bound.ipynb
@@ -107,7 +107,7 @@
    "source": [
     "#| label: vid-exp-bound-tail\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('HCEQFjHyLIw')"
+    "YouTubeVideo('HCEQFjHyLIw', extras=['title=\"Exponential Bounds on Tails\"'])"
    ]
   },
   {
diff --git a/content/Chapter_20/01_Maximum_Likelihood.ipynb b/content/Chapter_20/01_Maximum_Likelihood.ipynb
index 97b61340..8013ae37 100644
--- a/content/Chapter_20/01_Maximum_Likelihood.ipynb
+++ b/content/Chapter_20/01_Maximum_Likelihood.ipynb
@@ -151,7 +151,7 @@
    "source": [
     "#| label: vid-mle-discrete\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('AN6y89dfNCM')"
+    "YouTubeVideo('AN6y89dfNCM', extras=['title=\"Maximum Likelihood Discrete Case\"'])"
    ]
   },
   {
@@ -277,7 +277,7 @@
    ],
    "source": [
     "#| label: vid-mle-cont\n",
-    "YouTubeVideo('KPVK4t58zPY')"
+    "YouTubeVideo('KPVK4t58zPY', extras=['title=\"Maximum Likelihood Density Case\"'])"
    ]
   },
   {
diff --git a/content/Chapter_20/03_Prior_and_Posterior.ipynb b/content/Chapter_20/03_Prior_and_Posterior.ipynb
index 2812f25a..f7b70129 100644
--- a/content/Chapter_20/03_Prior_and_Posterior.ipynb
+++ b/content/Chapter_20/03_Prior_and_Posterior.ipynb
@@ -92,7 +92,7 @@
    "source": [
     "#| label: vid-pri-post\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('EXuoy618B-k')"
+    "YouTubeVideo('EXuoy618B-k', extras=['title=\"Prior and Posterior Densities\"'])"
    ]
   },
   {
diff --git a/content/Chapter_21/01_Updating_and_Prediction.ipynb b/content/Chapter_21/01_Updating_and_Prediction.ipynb
index cfe0a9d9..8a658e7a 100644
--- a/content/Chapter_21/01_Updating_and_Prediction.ipynb
+++ b/content/Chapter_21/01_Updating_and_Prediction.ipynb
@@ -173,7 +173,7 @@
    "source": [
     "#| label: vid-conj-pri\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('NBySphqHwvw')"
+    "YouTubeVideo('NBySphqHwvw', extras=['title=\"Conjugate Priors\"'])"
    ]
   },
   {
@@ -260,7 +260,7 @@
    ],
    "source": [
     "#| label: vid-pred-beta\n",
-    "YouTubeVideo('h7FmqsTfSKY')"
+    "YouTubeVideo('h7FmqsTfSKY', extras=['title=\"Prediction\"'])"
    ]
   },
   {
diff --git a/content/Chapter_21/02_Beta_Binomial_Distribution.ipynb b/content/Chapter_21/02_Beta_Binomial_Distribution.ipynb
index f13ecee3..33fec2f1 100644
--- a/content/Chapter_21/02_Beta_Binomial_Distribution.ipynb
+++ b/content/Chapter_21/02_Beta_Binomial_Distribution.ipynb
@@ -108,7 +108,7 @@
    "source": [
     "#| label: vid-nh-unif\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('_cc2xuBG6tY')"
+    "YouTubeVideo('_cc2xuBG6tY', extras=['title=\"Number of Heads (Uniform Prior)\"'])"
    ]
   },
   {
diff --git a/content/Chapter_22/01_Conditional_Expectation_Projection.ipynb b/content/Chapter_22/01_Conditional_Expectation_Projection.ipynb
index b8a07fa1..2cf10848 100644
--- a/content/Chapter_22/01_Conditional_Expectation_Projection.ipynb
+++ b/content/Chapter_22/01_Conditional_Expectation_Projection.ipynb
@@ -257,7 +257,7 @@
    "source": [
     "#| label: vid-proj\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('4-kX0R2ckMM')"
+    "YouTubeVideo('4-kX0R2ckMM', extras=['title=\"Projection\"'])"
    ]
   },
   {
diff --git a/content/Chapter_22/02_Least_Squares_Predictor.ipynb b/content/Chapter_22/02_Least_Squares_Predictor.ipynb
index a5d8282e..39a7813c 100644
--- a/content/Chapter_22/02_Least_Squares_Predictor.ipynb
+++ b/content/Chapter_22/02_Least_Squares_Predictor.ipynb
@@ -59,7 +59,7 @@
    "source": [
     "#| label: vid-ls\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('G_3rcPnInc0')"
+    "YouTubeVideo('G_3rcPnInc0', extras=['title=\"Least Squares\"'])"
    ]
   },
   {
@@ -145,7 +145,7 @@
    ],
    "source": [
     "#| label: vid-cond-var-def\n",
-    "YouTubeVideo('J-pCOpndoBI')"
+    "YouTubeVideo('J-pCOpndoBI', extras=['title=\"Conditional Variance  Definition\"'])"
    ]
   },
   {
diff --git a/content/Chapter_22/03_Variance_by_Conditioning.ipynb b/content/Chapter_22/03_Variance_by_Conditioning.ipynb
index 4a5226d4..6d220691 100644
--- a/content/Chapter_22/03_Variance_by_Conditioning.ipynb
+++ b/content/Chapter_22/03_Variance_by_Conditioning.ipynb
@@ -114,7 +114,7 @@
    "source": [
     "#| label: vid-var-cond-obs\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('H1aOoaIWLQQ')"
+    "YouTubeVideo('H1aOoaIWLQQ', extras=['title=\"Variance by Conditioning  An Observation\"'])"
    ]
   },
   {
@@ -220,7 +220,7 @@
    ],
    "source": [
     "#| label: vid-var-cond-form\n",
-    "YouTubeVideo('EWxRQv5k-uE')"
+    "YouTubeVideo('EWxRQv5k-uE', extras=['title=\"Variance by Conditioning  Formula\"'])"
    ]
   },
   {
diff --git a/content/Chapter_22/04_Examples.ipynb b/content/Chapter_22/04_Examples.ipynb
index 4f6fccea..568aedcd 100644
--- a/content/Chapter_22/04_Examples.ipynb
+++ b/content/Chapter_22/04_Examples.ipynb
@@ -59,7 +59,7 @@
    "source": [
     "#| label: vid-mixture\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('-uYGQrQCXzM')"
+    "YouTubeVideo('-uYGQrQCXzM', extras=['title=\"A Mixture\"'])"
    ]
   },
   {
diff --git a/content/Chapter_23/01_Random_Vectors.ipynb b/content/Chapter_23/01_Random_Vectors.ipynb
index b8d7ccfd..cd4f4fc7 100644
--- a/content/Chapter_23/01_Random_Vectors.ipynb
+++ b/content/Chapter_23/01_Random_Vectors.ipynb
@@ -176,7 +176,7 @@
    "source": [
     "#| label: vid-lt-vec\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('yE1FWjup0WE')"
+    "YouTubeVideo('yE1FWjup0WE', extras=['title=\"Linear Transformation\"'])"
    ]
   },
   {
@@ -284,7 +284,7 @@
    ],
    "source": [
     "#| label: vid-cov-mat-lt\n",
-    "YouTubeVideo('XvQa07eQDj0')"
+    "YouTubeVideo('XvQa07eQDj0', extras=['title=\"Covariance Matrix of Linear Transformation\"'])"
    ]
   },
   {
@@ -353,7 +353,7 @@
    ],
    "source": [
     "#| label: vid-const-cov-mat\n",
-    "YouTubeVideo('75RtwtxoRLw')"
+    "YouTubeVideo('75RtwtxoRLw', extras=['title=\"Constraint on Covariance Matrix\"'])"
    ]
   },
   {
diff --git a/content/Chapter_23/02_Multivariate_Normal_Vectors.ipynb b/content/Chapter_23/02_Multivariate_Normal_Vectors.ipynb
index 058ccc96..73fce2dc 100644
--- a/content/Chapter_23/02_Multivariate_Normal_Vectors.ipynb
+++ b/content/Chapter_23/02_Multivariate_Normal_Vectors.ipynb
@@ -164,7 +164,7 @@
    "source": [
     "#| label: vid-lin-combo\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('j6xxeJ3r4iU')"
+    "YouTubeVideo('j6xxeJ3r4iU', extras=['title=\"Linear Combinations\"'])"
    ]
   },
   {
diff --git a/content/Chapter_23/03_Multivariate_Normal_Density.ipynb b/content/Chapter_23/03_Multivariate_Normal_Density.ipynb
index 7723dd74..e8629922 100644
--- a/content/Chapter_23/03_Multivariate_Normal_Density.ipynb
+++ b/content/Chapter_23/03_Multivariate_Normal_Density.ipynb
@@ -93,7 +93,7 @@
    "source": [
     "#| label: vid-mvn\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('lUd7UVydEoI')"
+    "YouTubeVideo('lUd7UVydEoI', extras=['title=\"Multivariate Normal\"'])"
    ]
   },
   {
@@ -162,7 +162,7 @@
    ],
    "source": [
     "#| label: vid-mvn-param\n",
-    "YouTubeVideo('ao4fqXghbmI')"
+    "YouTubeVideo('ao4fqXghbmI', extras=['title=\"Multivariate Normal Parameters\"'])"
    ]
   },
   {
@@ -214,7 +214,7 @@
    ],
    "source": [
     "#| label: vid-mvn-def\n",
-    "YouTubeVideo('owX4JKA-2F8')"
+    "YouTubeVideo('owX4JKA-2F8', extras=['title=\"Multivariate Normal Definition\"'])"
    ]
   },
   {
@@ -274,7 +274,7 @@
    ],
    "source": [
     "#| label: vid-mvn-dense\n",
-    "YouTubeVideo('tIvp_0HPeIo')"
+    "YouTubeVideo('tIvp_0HPeIo', extras=['title=\"Multivariate Normal Density\"'])"
    ]
   },
   {
@@ -319,7 +319,7 @@
    ],
    "source": [
     "#| label: vid-quad-form\n",
-    "YouTubeVideo('rITtghijhb4')"
+    "YouTubeVideo('rITtghijhb4', extras=['title=\"Quadratic Form\"'])"
    ]
   },
   {
@@ -467,7 +467,7 @@
    ],
    "source": [
     "#| label: vid-mvn-vol\n",
-    "YouTubeVideo('g_S0UeHYc2k')"
+    "YouTubeVideo('g_S0UeHYc2k', extras=['title=\"Volume\"'])"
    ]
   },
   {
diff --git a/content/Chapter_23/04_Independence.ipynb b/content/Chapter_23/04_Independence.ipynb
index f3a50f9b..3359f9f8 100644
--- a/content/Chapter_23/04_Independence.ipynb
+++ b/content/Chapter_23/04_Independence.ipynb
@@ -68,7 +68,7 @@
    "source": [
     "#| label: vid-mvn-indep\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('sAaWBGxZYzc')"
+    "YouTubeVideo('sAaWBGxZYzc', extras=['title=\"Independence\"'])"
    ]
   },
   {
diff --git a/content/Chapter_24/01_Linear_Least_Squares.ipynb b/content/Chapter_24/01_Linear_Least_Squares.ipynb
index 8adaa795..81abcbed 100644
--- a/content/Chapter_24/01_Linear_Least_Squares.ipynb
+++ b/content/Chapter_24/01_Linear_Least_Squares.ipynb
@@ -61,7 +61,7 @@
    "source": [
     "#| label: vid-simple-reg\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('p-Dmvjh7MP4')"
+    "YouTubeVideo('p-Dmvjh7MP4', extras=['title=\"Simple Regression\"'])"
    ]
   },
   {
diff --git a/content/Chapter_24/02_Bivariate_Normal_Distribution.ipynb b/content/Chapter_24/02_Bivariate_Normal_Distribution.ipynb
index c6ec732c..56b55351 100644
--- a/content/Chapter_24/02_Bivariate_Normal_Distribution.ipynb
+++ b/content/Chapter_24/02_Bivariate_Normal_Distribution.ipynb
@@ -194,7 +194,7 @@
    "source": [
     "#| label: vid-constr-bvn\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('d-ucYswhZ_Q')"
+    "YouTubeVideo('d-ucYswhZ_Q', extras=['title=\"Construction: Bivariate Normal\"'])"
    ]
   },
   {
@@ -239,7 +239,7 @@
    ],
    "source": [
     "#| label: vid-constr-sbvn\n",
-    "YouTubeVideo('GQ9nsyr3umI')"
+    "YouTubeVideo('GQ9nsyr3umI', extras=['title=\"Construction Standard Bivariate Normal\"'])"
    ]
   },
   {
diff --git a/content/Chapter_24/03_Regression_and_Bivariate_Normal.ipynb b/content/Chapter_24/03_Regression_and_Bivariate_Normal.ipynb
index 855027e7..7665ef9e 100644
--- a/content/Chapter_24/03_Regression_and_Bivariate_Normal.ipynb
+++ b/content/Chapter_24/03_Regression_and_Bivariate_Normal.ipynb
@@ -158,7 +158,7 @@
    "source": [
     "#| label: vid-bvn-pred\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('akPEba4L-wo')"
+    "YouTubeVideo('akPEba4L-wo', extras=['title=\"Prediction\"'])"
    ]
   },
   {
diff --git a/content/Chapter_24/04_Regression_Equation.ipynb b/content/Chapter_24/04_Regression_Equation.ipynb
index 37a593c3..74fa3e00 100644
--- a/content/Chapter_24/04_Regression_Equation.ipynb
+++ b/content/Chapter_24/04_Regression_Equation.ipynb
@@ -143,7 +143,7 @@
    "source": [
     "#| label: vid-reg-eqn\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('hlAaKwSAXp8')\n"
+    "YouTubeVideo('hlAaKwSAXp8', extras=['title=\"Regression Equation\"'])\n"
    ]
   },
   {
@@ -225,7 +225,7 @@
    ],
    "source": [
     "#| label: vid-alt-form1\n",
-    "YouTubeVideo('SCY7fbSHnX8')"
+    "YouTubeVideo('SCY7fbSHnX8', extras=['title=\"Alternative Form I\"'])"
    ]
   },
   {
@@ -291,7 +291,7 @@
    ],
    "source": [
     "#| label: vid-alt-form2\n",
-    "YouTubeVideo('2kll2o4BZjI')"
+    "YouTubeVideo('2kll2o4BZjI', extras=['title=\"Alternative Form II\"'])"
    ]
   },
   {
diff --git a/content/Chapter_25/04_Multiple_Regression.ipynb b/content/Chapter_25/04_Multiple_Regression.ipynb
index 7be89616..0693370e 100644
--- a/content/Chapter_25/04_Multiple_Regression.ipynb
+++ b/content/Chapter_25/04_Multiple_Regression.ipynb
@@ -168,7 +168,7 @@
    "source": [
     "#| label: vid-est-mot\n",
     "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('VLIDNbHL8u8')"
+    "YouTubeVideo('VLIDNbHL8u8', extras=['title=\"Estimate Motivation\"'])"
    ]
   },
   {
@@ -309,7 +309,7 @@
    ],
    "source": [
     "#| label: vid-sig-noise\n",
-    "YouTubeVideo('qQG-yyUPgGE')"
+    "YouTubeVideo('qQG-yyUPgGE', extras=['title=\"Signal and Noise\"'])"
    ]
   },
   {

From 8a105612d7e91258d22b78925bef2fbdeea3fab1 Mon Sep 17 00:00:00 2001
From: Joshua Hori <jdhori@gmail.com>
Date: Tue, 28 Apr 2026 23:08:29 -0700
Subject: [PATCH 5/5] fix: convert Python video cells to pure Markdown iframes
 for accessibility

---
 .../Chapter_01/03_Collisions_in_Hashing.ipynb | 119 +--------
 content/Chapter_01/04_Birthday_Problem.ipynb  |  94 +------
 .../05_An_Exponential_Approximation.ipynb     |  42 +--
 content/Chapter_02/01_Addition.ipynb          |  92 +------
 content/Chapter_02/03_Multiplication.ipynb    |  81 +-----
 .../01_Functions_on_an_Outcome_Space.ipynb    |  85 +-----
 content/Chapter_03/02_Distributions.ipynb     | 121 +--------
 content/Chapter_03/03_Equality.ipynb          |  41 +--
 .../Chapter_04/01_Joint_Distributions.ipynb   |  81 +-----
 .../03_Marginal_Distributions.ipynb           |  41 +--
 .../04_Conditional_Distributions.ipynb        |  82 +-----
 .../05_Dependence_and_Independence.ipynb      |  41 +--
 .../01_Bounding_the_Chance_of_a_Union.ipynb   |  43 +---
 .../Chapter_05/02_Inclusion_Exclusion.ipynb   | 121 +--------
 .../Chapter_05/03_The_Matching_Problem.ipynb  |  81 +-----
 .../04_Sampling_Without_Replacement.ipynb     |  42 +--
 .../Chapter_06/01_Binomial_Distribution.ipynb |  81 +-----
 content/Chapter_06/05_Odds_Ratios.ipynb       | 121 +--------
 .../Chapter_06/06_Law_of_Small_Numbers.ipynb  |  81 +-----
 .../Chapter_07/01_Poisson_Distribution.ipynb  |  81 +-----
 .../02_Poissonizing_the_Binomial.ipynb        | 121 +--------
 content/Chapter_08/01_Definition.ipynb        |  81 +-----
 .../02_Applying_the_Definition.ipynb          | 121 +--------
 .../03_Expectations_of_Functions.ipynb        |  81 +-----
 content/Chapter_08/04_Additivity.ipynb        |  81 +-----
 .../Chapter_08/05_Method_of_Indicators.ipynb  | 121 +--------
 .../01_Probability_by_Conditioning.ipynb      |  41 +--
 .../02_Expectation_by_Conditioning.ipynb      |  84 +-----
 .../03_Expected_Waiting_Times.ipynb           |  93 +------
 content/Chapter_10/01_Transitions.ipynb       | 121 +--------
 .../Chapter_10/02_Deconstructing_Chains.ipynb |  81 +-----
 content/Chapter_10/03_Long_Run_Behavior.ipynb | 121 +--------
 .../01_Balance_and_Detailed_Balance.ipynb     | 121 +--------
 content/Chapter_11/02_Code_Breaking.ipynb     | 241 +----------------
 .../Chapter_11/03_Metropolis_Algorithm.ipynb  |  81 +-----
 content/Chapter_12/01_Definition.ipynb        | 121 +--------
 .../02_Prediction_and_Estimation.ipynb        |  41 +--
 content/Chapter_12/03_Bounds.ipynb            | 121 +--------
 content/Chapter_13/01_Covariance.ipynb        |  81 +-----
 .../02_Properties_of_Covariance.ipynb         | 121 +--------
 .../03_Sums_of_Independent_Variables.ipynb    | 121 +--------
 .../04_Symmetry_and_Indicators.ipynb          | 121 +--------
 .../01_Exact_Distribution_of_a_Sum.ipynb      |  81 +-----
 content/Chapter_14/02_PGFs_in_NumPy.ipynb     |  81 +-----
 .../Chapter_14/03_Central_Limit_Theorem.ipynb |  81 +-----
 content/Chapter_14/05_The_Sample_Mean.ipynb   |  81 +-----
 .../Chapter_14/06_Confidence_Intervals.ipynb  | 121 +--------
 content/Chapter_15/01_Density_and_CDF.ipynb   |  83 +-----
 .../02_The_Meaning_of_Density.ipynb           |  41 +--
 content/Chapter_15/03_Expectation.ipynb       |  41 +--
 .../04_Exponential_Distribution.ipynb         |  81 +-----
 .../01_Linear_Transformations.ipynb           |  41 +--
 .../Chapter_16/02_Monotone_Functions.ipynb    |  41 +--
 .../01_Probabilities_and_Expectations.ipynb   | 201 +--------------
 content/Chapter_17/02_Independence.ipynb      |  41 +--
 ...3_Marginal_and_Conditional_Densities.ipynb |  81 +-----
 ...ta_Densities_with_Integer_Parameters.ipynb |  43 +---
 .../01_Standard_Normal_Basics.ipynb           |  85 +-----
 content/Chapter_18/03_The_Gamma_Family.ipynb  |  41 +--
 .../04_Chi_Squared_Distributions.ipynb        |  41 +--
 .../02_Moment_Generating_Functions.ipynb      |  81 +-----
 .../03_MGFs_Normal_and_the_CLT.ipynb          |  41 +--
 content/Chapter_19/04_Chernoff_Bound.ipynb    |  41 +--
 .../Chapter_20/01_Maximum_Likelihood.ipynb    |  81 +-----
 .../Chapter_20/03_Prior_and_Posterior.ipynb   |  41 +--
 .../01_Updating_and_Prediction.ipynb          |  81 +-----
 .../02_Beta_Binomial_Distribution.ipynb       |  41 +--
 ...1_Conditional_Expectation_Projection.ipynb |  41 +--
 .../02_Least_Squares_Predictor.ipynb          |  81 +-----
 .../03_Variance_by_Conditioning.ipynb         |  81 +-----
 content/Chapter_22/04_Examples.ipynb          |  41 +--
 content/Chapter_23/01_Random_Vectors.ipynb    | 121 +--------
 .../02_Multivariate_Normal_Vectors.ipynb      |  43 +---
 .../03_Multivariate_Normal_Density.ipynb      | 243 +-----------------
 content/Chapter_23/04_Independence.ipynb      |  43 +---
 .../Chapter_24/01_Linear_Least_Squares.ipynb  |  41 +--
 .../02_Bivariate_Normal_Distribution.ipynb    |  81 +-----
 .../03_Regression_and_Bivariate_Normal.ipynb  |  41 +--
 .../Chapter_24/04_Regression_Equation.ipynb   | 121 +--------
 .../Chapter_25/04_Multiple_Regression.ipynb   |  81 +-----
 80 files changed, 327 insertions(+), 6333 deletions(-)

diff --git a/content/Chapter_01/03_Collisions_in_Hashing.ipynb b/content/Chapter_01/03_Collisions_in_Hashing.ipynb
index 615a1181..218cb0e4 100644
--- a/content/Chapter_01/03_Collisions_in_Hashing.ipynb
+++ b/content/Chapter_01/03_Collisions_in_Hashing.ipynb
@@ -33,66 +33,12 @@
     "What if the hash values were just assigned at random, without taking into account which of them have already been assigned? If there are a large number of distinct values and a relatively small number of individuals, then it seems reasonable to think that the chance of a collision will be small. For example, if there are 1,000 available hash values and only 5 individuals, it doesn't seem likely that you'll get a collision if you just pick a random sequence of 5 values for the 5 individuals."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": "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",
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/uFLsmVFcHXw\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x106471630>"
-      ]
-     },
-     "metadata": {
-      "scrapbook": {
-       "mime_prefix": "",
-       "name": "vid_prob_model"
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#| label: vid-prob-model\n",
-    "#| echo: false\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"uFLsmVFcHXw\", extras=['title=\"Probability Model\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
+   "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-prob-model)\n",
+    "```{dropdown} 🎥 Probability Model\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/uFLsmVFcHXw\" title=\"Probability Model\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -126,65 +72,12 @@
     "If you look back to Part (i) in the example about random number generators in the previous section, you will find that it is the same as our current question, in the case where $N = 10$ and $n=2$. We can just follow the same process to get our solution here."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": "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",
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/cD26s3DW-J8\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1070cfe20>"
-      ]
-     },
-     "metadata": {
-      "scrapbook": {
-       "mime_prefix": "",
-       "name": "vid_collision_prob"
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#| label: vid-collision-prob\n",
-    "#| echo: false\n",
-    "YouTubeVideo('cD26s3DW-J8', extras=['title=\"Collision Probability\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
+   "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-collision-prob)\n",
+    "```{dropdown} 🎥 Collision Probability\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/cD26s3DW-J8\" title=\"Collision Probability\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_01/04_Birthday_Problem.ipynb b/content/Chapter_01/04_Birthday_Problem.ipynb
index 3298c1d1..ad303cf6 100644
--- a/content/Chapter_01/04_Birthday_Problem.ipynb
+++ b/content/Chapter_01/04_Birthday_Problem.ipynb
@@ -58,60 +58,12 @@
     "The problem is commonly solved under the assumptions that each year consists of 365 days and that each person is equally likely to be born on any of the 365 days regardless of the birthdays of others. "
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": "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",
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/A88MJdZLe3A\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x103cb33a0>"
-      ]
-     },
-     "metadata": {
-      "scrapbook": {
-       "mime_prefix": "",
-       "name": "vid141"
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#| label: vid-bday-assum\n",
-    "#| echo: false\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('A88MJdZLe3A', extras=['title=\"Birthday Assumptions\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-bday-assum)\n",
+    "```{dropdown} 🎥 Birthday Assumptions\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/A88MJdZLe3A\" title=\"Birthday Assumptions\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -156,50 +108,12 @@
     "$$"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/ci7bPVOpfsk\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10ec2c700>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-note-code\n",
-    "#| echo: false\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"ci7bPVOpfsk\", extras=['title=\"Notes on the Code\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-note-code)\n",
+    "```{dropdown} 🎥 Notes on the Code\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/ci7bPVOpfsk\" title=\"Notes on the Code\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_01/05_An_Exponential_Approximation.ipynb b/content/Chapter_01/05_An_Exponential_Approximation.ipynb
index 3a9e7e6c..504bc5b2 100644
--- a/content/Chapter_01/05_An_Exponential_Approximation.ipynb
+++ b/content/Chapter_01/05_An_Exponential_Approximation.ipynb
@@ -62,50 +62,12 @@
     "The main steps in the approximation will be used repeatedly in this course, so we will set them out in some detail here."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/rUhunl890KM\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1031a9d30>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-approx-collide\n",
-    "#| echo: false\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"rUhunl890KM\", extras=['title=\"Approximate Chance of Collision\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-approx-collide)\n",
+    "```{dropdown} 🎥 Approximate Chance of Collision\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/rUhunl890KM\" title=\"Approximate Chance of Collision\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_02/01_Addition.ipynb b/content/Chapter_02/01_Addition.ipynb
index 459d5842..51a3fa9b 100644
--- a/content/Chapter_02/01_Addition.ipynb
+++ b/content/Chapter_02/01_Addition.ipynb
@@ -242,56 +242,12 @@
     "This is sometimes called the axiom of *finite additivity*. Kolmogorov actually specified countable additivity. We'll come to that later."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": "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",
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/KaOriB3Zep8\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x176f66b30>"
-      ]
-     },
-     "metadata": {
-      "scrapbook": {
-       "mime_prefix": "",
-       "name": "vid_axioms"
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#| label: vid-axioms\n",
-    "#| echo: false\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"KaOriB3Zep8\", extras=['title=\"Axioms\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-axioms)\n",
+    "```{dropdown} 🎥 Axioms\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/KaOriB3Zep8\" title=\"Axioms\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -397,52 +353,12 @@
     "What's the chance that the student is in the bright blue difference? If you answered, \"50% - 40% = 10%\", you are right, and it's great that your intuition is saying that probabilities behave just like areas. They do. In fact the calculation follows from the axiom of additivity, which we also motivated by looking at areas."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": "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",
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/eEoogMY8z60\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x105715150>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-diff\n",
-    "#| echo: false\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"eEoogMY8z60\", extras=['title=\"Differences\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-diff)\n",
+    "```{dropdown} 🎥 Differences\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/eEoogMY8z60\" title=\"Differences\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_02/03_Multiplication.ipynb b/content/Chapter_02/03_Multiplication.ipynb
index 8bfa5e85..1d7e5348 100644
--- a/content/Chapter_02/03_Multiplication.ipynb
+++ b/content/Chapter_02/03_Multiplication.ipynb
@@ -154,49 +154,12 @@
     "$$"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/Nd_gWqoMah4\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10c40bc10>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-frac-frac\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"Nd_gWqoMah4\", extras=['title=\"Fractions of Fractions\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-frac-frac)\n",
+    "```{dropdown} 🎥 Fractions of Fractions\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/Nd_gWqoMah4\" title=\"Fractions of Fractions\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -251,48 +214,12 @@
     "We are dividing by $P(A)$, and the more careful among you might be wondering what happens if $P(A) = 0$. Well, in that case we wouldn't be given that $A$ happened, because $A$ can't happen. So that's not a case we have to worry about."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/YE9p2cprkOI\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10c438400>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-cond-prob\n",
-    "YouTubeVideo(\"YE9p2cprkOI\", extras=['title=\"Conditional Probability\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-cond-prob)\n",
+    "```{dropdown} 🎥 Conditional Probability\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/YE9p2cprkOI\" title=\"Conditional Probability\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_03/01_Functions_on_an_Outcome_Space.ipynb b/content/Chapter_03/01_Functions_on_an_Outcome_Space.ipynb
index 99286beb..768d87ab 100644
--- a/content/Chapter_03/01_Functions_on_an_Outcome_Space.ipynb
+++ b/content/Chapter_03/01_Functions_on_an_Outcome_Space.ipynb
@@ -361,51 +361,12 @@
     "five_rolls_space"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEABALDBoXFhoaGRoeHRsfIjMmIyIiIzUtKikyMio9MjI4NjE+PFBFODhLPy03RWFFS1VWW1xbNUJlbWRYbFBZW1cBERISGRYZLxoaLlc2NT1XV1dXV1daV1dXV1dXV1dXV1dXV1dXV1dXV1dXV1dXV1dXV1djV1dXV1dXV1dXV1dXV//AABEIAWgB4AMBIgACEQEDEQH/xAAbAAEAAgMBAQAAAAAAAAAAAAAABAUCAwYBB//EAEcQAAIBAgQCBwUECAMHBAMAAAECAAMRBBIhMQVBBhMiNVGCszJhcYGRFKGx0RUjQlJUk8HwU2Jyg5KjssLS4TNDovEWJCX/xAAXAQEBAQEAAAAAAAAAAAAAAAAAAQID/8QAHREBAQEBAAIDAQAAAAAAAAAAABEBEgJRIWHwE//aAAwDAQACEQMRAD8A+fxEQEREBERAREQEREBERAREQPrfQkH9E0Muhs9vj1jS4RK11zMpAGoA3Nj/AOPpKnoL3VhvP6jS8pVM1zsL29/zlRpw61g36xlK25DUnT/z9YVawYaoUvrcdq2vhp4TbWvaymxIO2+kypk635G0COoxGtzTtY2sDfbT75IpBsq5iC1hcja/OZRCkTxr2NtTymKMToRY2vCM4iIUiJoAbPbObby5iN8Sr6RcZGBw5q5C7bKANL+LHkJD6G4zGYjDtUxY0Zr0yRYkfDw8DIroImg1Lk2JFth4+/4TN2Ow3teWJWyJqpPe9rkDmd/hNS4rMRYNpe4tcxmaVKia3ZtlAva+sUHuva3G95INkSJTrEsO1e59nlaS5dyGaRE0PWs9r8wLeN5Mwb4iIUiJhUew2JhGk06vXFusHV5fZsL3+P3zWKeJ/wARPp/4/vX5TAZ4hNzfkdNIEOqmJLHKygeHy/OVvTe/6JxF9+xf+YsvKjHWxtYXlF03a/Ca58Qh/wCIskiZkfJIiIaIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiIH1zoN3Vh9P3/UaXwHu3lJ0E7qw3n9Vp0EDUy33EKLbCbYlo1390fKbIkGvXwngFuU2xA1390a+E2RA1390xydrNY3taboloi18KlX21zArlKknKQTfUbHabraWtpNkSDRTphdh+MyZb7g/fNsS0awLaATBaYDFgNTvvN8RRqZb8j8rxl0tbSbYijUq5RYCe6+E2RFGu58J4V1BtqPjNsRRr18I18JsiQa9fCD8JsiBr18J4Ba9hvvNsQNL0w3tLeUXTjThWIFtOx6izo5z/TvurE+T1Flo+QRESBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERA+v9BO6sN5/VadBOf6Cd1Ybz+q06CAiIgIiICYVaqopZiAoFyTM5XcfDnCVBTF3JUDS9ruBe3MDf5QJmGxC1UzLe1yLEWIINiLfKbZT08RXoUMgwrvUUbqylWYnVr3vuSTpPUxWONJAcOvWs1mYsAii51sCTty8YFrUcKpZiAoFyTyEww2IWrTWohJVhcXFj9DOd4nV4jiKa4enhxTLL+squwy3B1C2J0NuY57Tdh2xynIKQpUaYAGzlgBcm97lieVh8YHQzTi8StGk9V75UUsbeAlfhcXjWANTC01Gmgq9rVQTpa2h5XlpUQMpU7EWPzgRMNi2q16gUDqUAGbmWIzae4Aj6yWlRWF1IIva4N9RvKmvwd1wYw1CqVue27XzMD7Wo2J/u0wXo4BRo0RWqZKb5rA5dLG4GWx1J3N4FyKi5stxmte19beMjcSxRpYepUSxYCy31GYmw+8iaGwISjVpCtkRr5baFAdwDfb6byufBYZaD0XxqhGJYBWRQrXuCLa6G2hPLW8C0x2LamcMoIvUqhGPuCMx+HsyRhMbSrrno1FqLcjMpuLj3yio4XBGj1bVetOfOTTzLqVy6BNhlNvfvvJnBMJ1elOrX6hOytOpTRQRbkcobT3wLiIiAiIgIiICIiAiIgJz/AE77qxPk9RZ0E5/p33VifJ6iwPkEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERA+v9BO6sN5/VadBOf6Cd1Ybz+q06CAiJD4rjTh6XWBA/aAIL5QLm172PjAmRIlDFm+Wt1VNjsoq5ifqBJQMD2Rq2Hd2uK7otrZVC7+NypkmVp4oTjvsyqCq0usqOT7NzZR9xgbvsT/xVf8A4f8A2Tz7A/8AFV//AIf9k0cT4vTp0BVp1UYdYq3UhtM4D2tzAvN9PitFrsrqUA1fMMoN7ZTrcN7oD7A38TX+q/8AbPRgD/j1j5h+U10uN4dyo6zKWtlDAgm+gOvIkWv46SahJvta+lvCBo+wDnUqn/aGeDh1PxqH41X/ADkuIEQ8MoHdL/Ek/wBZj+iMN/gU/moMmxAirw3DrtQpDyD8pvSkq+yoHwFpnEBERAREQEREBERAREQEREBOf6d91Ynyeos6Cc/077qxPk9RYHyCIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgfX+gndWG8/qtOgnP8AQTurDef1WnQQEicSwK4mn1b+zmViLXBCsDY+42kuIFZj+DJUprTpZaIzXbKu4sdNLeN/lzmtuCN2hTxNSkpJZVQKApK5ddNf756y3iBV4fhL0kC08TVByZWLWa55MAdFI92kiUOilBabq71XaoLVG6wrn8LgH++cv4gVNDo7hqdM0wKhQj2TUYgG1rgX30m+rwbDO7u1IFnAubn9nYjXQ+8ayfECG3CsOQVNJSCoUgi9wpzD6E3kmlRVL5FC33t8LfgBM4gIiICJ5eewEREBERARMKtZEF3YKPEm0zgImKuDexBsbH3H+zMK9ZUALc2Cj4sbD8YG2J4SBNVDELUVWGzbX5/3aBuiQ8Di86sXIBFV0HK+VyB9wkhq6BQxYWJAv7ybD75dzc2JWyIiRSc/077qxPk9RZ0E5/p33VifJ6iwPkEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERA+v9BO6sN5/VadBOf6Cd1Ybz+q06CAmC1VLFQwLDcA6j4iZznsPVHWgOjKadV2apkJL3JsBYXtYi9/3RvvNZ41N2OhkXC45KmbVQQ7LbMLnKxF/ulZQquQvXYhwQuXs0msw8TdfaP3TE0sOKYRKii1QvY0yQQSSAfG1x9JrhOl8WAgmc+MNROYtWq1GYEXFMki5vp2fp4TNsHSIfLTxZD3BsTseXaOn4++OM9lXSYhGUurKyi+qm4032kDBcUFZgc9FVOyZwanuvrp8J5g6vU0xTTDV8q7dlB+BE11EFRiz4bEN4AlAF+AzDX3nWOcKtFxCG5DqQDY2YafGasVjkpWDXJIJAUEmw3OnL3zn8LwzrcPTD4eo2m5qqoIFxaw5an3ybU4dUcLeghyrlu+Ie9jyOVdR7pePHNlS7Fth8VTqgmm4YA2NjI3EOJ06SPlq0+sGliwuCTa5Huvf5TTg8JXoqVppQRb3tnd/xm37Pif36A/2RP/VJMzfpbsRarUMO1HLVTrmYZiWGZ1PtFvdzH3SzoYylUJWnURyNwrAkfSRWwddvaqUf5P5tJOEosgOdlY3/AGUyybJ9mJEREw0RExqZspy2zcr7QKziWGariKQFwvVVMxtvcr2b8r6677zWMViWSnVp0+y1wKehygDS503I5be+SamIqirTpFkDVAzCyE6La/7X+YRh2NbPlxDkKxUlUUC43sSDe23xvN34jMQsMmORqgC07As1z+2TtryA/p79M8TQxr0FB6k1VdXBBIGhvaxHLxkmki1HKitXa25BstwbEXAAvNx4fTO5qH41X/7pe/mnLRhcJiAafXVEqKASyldc17qb87bco4dhKtFUV6yMqCwXJ9977zRhqaPiKlPqb01uA5diSy5b3vp+1bf9kyf+jKH+Cn+7Jvl+hFa2Bw6B1fEKocvmBexIc35ncX3+PjNFDDYSkKapibqjZgt7je+gXS9+djL5MPTX2UUfACbbS/005R8PjEqkhc2nMowH1IkiInNonP8ATvurE+T1FnQTn+nfdWJ8nqLA+QREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQERED6/0E7qw3n9Vp0E5/oJ3VhvP6rToICQOJcTGHKAqTmvqTZRa255bz3jOJalh3dbg6AsBfICQC1vcDf5TVg6NHE0E0c01YizOSGsdbkEhxfXmIFhSLFQWAVuYBvb5zORsfhDXp5BUqUtQc1M2OnK/hPcFg1oJlUs3Ms7FiT7yYGdbFU6YJeoqgC5zMBYTPOPEfWUnF+HdZisPlo0yHqBqjspJ7C6A6acufKeYrhb1eIpUWnTREUOahXMXY9m24tYC/Mag7wLYY6iWKiqmYNlIzC972tbxg46l1vU5x1nhr4X32vbW0rKvCnOMpsuVaK56lwoLB2tzJO92OgjheCxVKvUNRgUeozk3BvcWXTLcMLDnawgXcSv4ngatdqWSsaSKbuFuC2o5gjkCLe/3TGlwtlY3xNbLc2QHQKTe1zc/O94GNHiyfaMTTeon6tlCganVATtvqZKPEqHVmp1q5AbZr6X8B4mY4LhqUGrFAAtQg5QLAWQL/SRV6OYYCmCHYU/YDVGOXS1hroIHrdIsKtszspNrA02ubi62FuYGnjIn/wCTipXSnQph0srPUdsgAYX0G5IHLx0l1SwtNL5UVbkE2A3Ayj7haZpSVdlA56CBCwWONWvWUMpRQpWwOxHM33uDpbwlhPAJ7AREQKbiH/7GJWhS0dFIq1RvTV7XVT++2X5AX8Jp4Tj0fFPQR+rWhdEoKhsQoALMbeJ0AO3jfS3ZqNA3Jp0zVfmQudjp8zsJuAFztfnAh8M4YmG6wqSTUbMdAAPcANv/ADJlQkKcoBa2gJsL+8yvxPF0HUiiUqmpUyXVrqoAzOSR4D8RJr4hFpmoWHVhSxa+lgLk3gc9g6OMfD56NYAvWqEhVAFs7C4JvfUDw0k1eEPUq3xDmpR6tf1ZckZ9cxI0uLWtf6TavHMOosQ9NRl1emUUZjZTcgaE85F4zxWnUwzfZ6iVGDqSA9iAtQEk87WHhsYF1QorTQIgso2HhNkqKvEa9OulOotNEIzZwHYHW2UWG/PX6Ga34linBNKgUKuFs6k5rvb3ZRbtE67jeBdxOertxWrSIVaFJmzLubgcnvra+1td735Sy4LRq08OqVyxcblnzk/O23xgT5z/AE77qxPk9RZ0E5/p33VifJ6iwPkEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERA+v9BO6sN5/VadBOf6Cd1Ybz+q06CAngFtp7Oe4pj+spFXXqlFVVZnuLfrABa4AJ0vpoIHQzxmAFyQB75hQqK6AowZeTA3v85z/AA2nmxJFalRrOWctUF2KC/ZBJWw8Mu+kDo1YEAggg8xNeJxCUkzucq3Av7ybD7zIeOaua1KlTGSk4OeoB2lsL2HIXta5lbWwOLrU64qLcowNAMw1y1Mw1HioAudd/mHQU6ivfKQbEqbeI3EzlFT4TW+y1aLlS7VmqKwYgatn1Ftrm2XmJZY3CNVoGmHyMQO0BpoQbWvsbW32MDF8XU+0dWiBkXLnN9RmzfhlH1ktnAIBIBOwJ3+EoOBpULVsjhVHVjMUuz9j2t9L3vbeSMRwJquK+0NiHXL7AVVuulrXYHTc6Ab/AAgXMq8RxhRiqOHplGJzGrr7Cqu58NbbyelGyZCzNpYsTqb+8SBhujuEpoEFFSBbVtToLb+HugYY7jyUadJghc1mIpBWFmFrg38CLW56yxw2KSqDkYHKbNY3sbXtfbnMH4fRZOrNKmUvfLlFr+NpupUlRQqKFUbACwHygZxEQETE1VH7Q+sCop2YH5xBVY3gYq1+vFQh8tlzDP1fvQHRT9dhMOH9HVoq4NevUNT2yzDtfHS8uSwHMSBxDiIRGFJleuRZEGtzyuBy98uZupWtej2G7F1Zsns5nY6Wtl31XbT3SXS4fRRXVaahKl8627JvvptreSYkVGw/D6NJSqILHe+t7bbzeaa69ka76TKICIiAiIgJz/TvurE+T1FnQTn+nfdWJ8nqLA+QREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQERED6/wBBO6sN5/VadBOf6Cd1Ybz+q06CAlbxbBvWbD5QCFqgtfkB2rj5qB85ZRA8tPbREBERAREQIuAwC4dWClmzG/a5AAAD4AATdWrKgBY2BIF/jtNk8ZQwIIBB3BgexIX6OUf+m9WmPBX0+huBBwBbR69V1/duFv8AEqAfvlmI9qY67FKSmq40NjZVP+ZuXw1MCjiG1NZV9ypcD5k6/dJNKmqKFUBVGwAsJnF9CtXE4gs1HIM671bHJY7EDm24y35bzb+jEbWqXqn/ADsbf7o0H0m3AYtcRSWqnste3vANr/O14x+KFCi9VgSEF7DnLfRGv9FYb+Ho/wAtfyg8Iw38PS+SAf0kirXCZb37TBRYE6n8JpwOKNZEqAAK1766ixsNLSdb7JjAcIw3+BTPxUGSKOGp0xamiIP8qgfhIvCcaK1EsXVmV3DbaWcgX8NBNuPxyUKfWNcgkKoFtSTYDU2i6sSolfW4vTpWFUFXydY6jtZFva7Ecvf7jItLpLRdmCq3sM9M3X9bl5KL3ueVwLyC6ic1S6W0w1JajUr1NSUfSnrbKxP7Wvu2Pz6NHDAFTcHUGBlERAREQE5/p33VifJ6izoJz/TvurE+T1FgfIIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiB9f6Cd1Ybz+q06Cc/wBBO6sN5/VadBAREQEREBERAREQEREBERA54jF1q2LNKqUWnmWmLixcKpUWtot73O5v7ptq4HFZ1VGOQqOsZnvmN+0COQttltufCxuadFUzFRbMcze8+P3TOBz3COFYvD0HQVFVhTy0wTmAa5Oa1gANbADw1Jntfg+KOFrUxXDPUsQGubEW/aPiRfb4AToIgU/DuG4qi6s2Jzgg50YEgEkHs632Ftb8/hMsHwipSa32ljSGYCnlFsrG9idydbX8PrLaIHNcU4bQpYCvkY5ir0xlbKGZmNkIG4DNYCXOI4dTr00SspZVt2bkC9ra23nv6Modb1vVL1l73tsfEDYH37yXAonwNMY2hTyIUVCUW3sgAcviBaXNKgiXyIq3NzlAGs2WiAtERAREQEREBOf6d91Ynyeos6Cc/wBO+6sT5PUWB8giIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiIH1/oJ3VhvP6rToJz/QTurDef1WnQQERECr4zj3pArRK51Q1GJ1sq+7xJ0+vhLNTcAyhxnCa1bGVayvkVVpqqsLpVyksc3PQn/7m2uvEHrIV6ukijUZ8wZr8+zcrbwIMC6lbjeLrRxWGw2Us9ct5Qovf7ppTC4/O9Q4iluMtMJ+rK21vrmDX53+Ug0OCY77ScZUr0TX1RUKE01Q8hqCD74HS3ns5jCdHariu+LNKpWqJZW1azC+VgbAoBcaDw3kmhwjF0rEY0kKwZVcXBuO0GN7kX2sRaBb42o6UajUwGdVJUHYkDaQE4o1XE4ZKVjSeia1Q+ANsn9ZLOOpIo62tSB56gD6Emc6mAwy0MRSGLLdaQAyLmy01JKpYA3GpHvvA6iliEcXR1YXtcEHXw+MJiEY2Vg2pU5dQCBextt85zC4Kh1NaklfFnrddKDAK1gAVtTFrWGl+UnlKd6nV0cUoekKRVEyAAXsRe1m1tf4QLLieI6vC4ioDqlN205EKTKJ+MJX4RWZKhNSnQ7RBIObLuCNdwfpNfD+Bph0dEw2LqK4s2erTUEWtqoYA6cyCZL4fwypQQotDOCgT9fWHsjZbIlramBZcN4tRxOYUmLFQLmxsdSNDz1Bk+cynRkqSaSU6F91p1aoB+SssvsDSdKYV8lx+4CBbluSbwJEREBERAREQEREBERAREQE5/p33VifJ6izoJz/TvurE+T1FgfIIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiB9f6Cd1Ybz+q06Cc/0E7qw3n9Vp0EBPGvY235Xns11qyU1LOyqo3LGwgQEbHm91ww101c3+6ZdVjT/7uHT4Umb/AKxN2F4jRrNlpuGNrjQ6jxB57yXArxhMSfaxQ8lID8SZ6OHOfaxVc/7o/BRJ80UcUrtVUX/VNlYna+UNp8mECN+h6d+1UxDe413t9AZkODYbnRVv9d2/5ryZ1gsDcWO2u/wmUDRRwdKn7FKmn+lQPwm8CJFr8Ro03FN6gFQi4XdreNhygSomihjaVUkU6iuVAJCm5F9rzXxYOcLWFO+fIcttDe3L3wNtPF0ndkWojOvtKGBYfEcpunO47FYSnRwjUnpUz1idXqFNswD3vyykg/nLzDYlKy56bB1va42gYriwcQ1CxuKYe/IgsRb5ZfvkiUuK4L1mLSqXqFCjq/61lIJIK2ykaDWe8CwOMoXXEV1qUxfKPabfQ5iAduWvxgWWNqFKNV1sGVGIv7hec9h8b17ItGrWOJz6g1SVVVYZiw0XVTtbcj4zpK9IOjIdmUqfmLSpwPRulSoCkWdiH6xXBysrZQpKkajb74FsldGd0VgWS2YDcX2vNkiYTAJQQrTJBZszMTmZjzuTve1vwkuAiIgIiICIiAiIgJz/AE77qxPk9RZ0E5/p33VifJ6iwPkEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERA+v9BO6sN5/VadBOf6Cd1Ybz+q06CAlbx7CtUoqyKGqU6iOlxe1mF/uvLKIFVXqYuk1NiBWW5DrSQDl2faa+/Oa0r8Qq5AKVPD2DZ2ezgn9kKFa9vEmXMQKysMYzUmApgKTnQOe0baa5dt9PhrI+H4EVOIYv+sqMGWpc3U5QDpf2bjbwl3ECrx3COvdM7g0VKN1Zpg6ob6HkDsdNvCa24AllIqOKiVM6Ne4TW+ULsFsbS4iBjTUgAFix8TbX6SE/CKbV+vzOGuCVB7JKiwO19L8jJ8QImF4ZRolTTWxVSt7m5BOY38TfW5jEcNoVXz1KSO3iwv+MY7iFOhbNmZmvlRAWZrb2A5Dxkik+ZVaxFxezCxHxHjA8SgigAIoA2AAsPhNkTVTxCOQFdWuMwsQbi9r/C8DbERAREQESCMaTi+pABUUw5I3BJNvlpJrsFBJ0AFzA9icfT4q9Stg0KVjZWrFV9uoQxUZhfsqNT2juALS2wnSEVsX9nFJ0XLfNUujX8MhA+G/ygXU04zErRpVKreyiljbwAvIFbFYl8TUo0lVURFbrGUnMTfsgXAvpvf5SEtTGYvBYmnWoZHaiQulu0QRlsTry103gXdPFI1R6YPbRVYi3Jr2/wCUzVj8G1VqBV8op1Q7DXtDKRb75UcQ4RXqmqwA7VCkAC1rujFrN4rrqOd5e4QMKSBlCMFF1U3ANtgeYgboiICc/wBO+6sT5PUWdBOf6d91YnyeosD5BERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQPr/QTurDef1WnQTn+gndWG8/qtOggJU161WpVrBMQKIpOqgFVIN0VtSdf2uVpbSsfg9PrqlbOys5BOiclC+0VuBZfGBLbHUg5pmoocEDKTY67TVT4rSesaKFmYXBIUlQRuC21/dIOLwOFat1z4jKwQICKgBABJIvzBvqDflNQwvDVvYZwSTYZ6gGY3awF7XPIQJZ6R4MFr1lsq5i1jl0IBs2xNyNB4zHj3H6eCpk2z1CuZVG1vEnkPxmmlRwaimKeFqMKalU/UvoG9r2gN5Gr8OR6lLLgX6hFYGn2FBYshB9vlkgacLx4YbrA5etVq4rIqX1HYXl+yPAe+dI9dHSplJfLuEOt7XsDfeVteh1xJqcNDEixLmnqBtsTJeD65SqdRSpUhyV9R8FC2++BPiIgUXEcZTWq9enUUVsP+rZG2cMA2Uc821iOekvFNwDa3xmv7LT6zrOrTrP38ozfWbYCaMNg6dIWprbUncm2Y3Nr7C/Lab4gUeI4ji/ta06VA9XlIu4st76MWF7DQi2+o2meHrcQIpFqNIWW1QGpYs1hqLAgLe/v1lzECkTDcSa+bEUVuA3ZS9mvquo9m1td/hLLHYXr6LUizJnFiybj4X+kkxA5vg+Dp0sfXo0i6qlNCxuO22Zj8gLjQWE6SRaXD6aV6lcA9ZUADa6ae7+9pivFcOX6sVqZe9rBhuBcj42F4G+lh0T2VA/HU33+JmwoDYkAkbTThsUKoLKrBf2WIsGHiPd8Zq4hinor1uVWpLrU/eA5sORAGtoEyJjnFr3FrXvytPErKy5lYMviDcQM4nP4DHJjMhGMqJUYk9XTKaWO3snlyJvOggIiICc/077qxPk9RZ0E5/p33VifJ6iwPkEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERA+v9BO6sN5/VadBOf6Cd1Ybz+q06CB4RIY4Vh+dFGPi4zH6teTZXdIqjJgcS6EhlplgR4jWBMp4amns00X4KBNoEqcCzVcbiHztkpWphL6aqHJt46yxxWJWjTao5sqi5sLwNsSrp8YDYvqEyOpuLo92Uga5ltoOV7nW0242nXNWn1bOKf7QXINb88wJtbwgTK9ZaaM7sFRRdidgBMMNikqrmQkjxsR+IkLpE1sHV8o+rgTdxenUaj+qvmDo1huQHBYfQGBMZgNzaY1KqoAWYKCQBc2uSbAfEkyDxzDVKtALTF2FWm1r2uFqBj9wmfFcK9amgQgFa1N9fBHDH7hAlYiutJC7nKo3J2E2SLxLCDEYerRJt1iFb+FxN9JSFUE3IABPibQNVOouIpBkZgrXsRodDb+kicKuKuLUu7ZKigB2LWHVqefiSZIwmG+zUAi3qZbkDQE3Ym33yNgC7YqtU6qpTRkUHrLC7AnUWJ5EfQQLGrUCKzG9gLmwufpObPGmbFUxVLUkU1CtNdXqkNkVco8NT+Vp080JgqSuHFNQ4uA1te0bn6mBWUOk+HqYinQQksy5ibgZN9G10Omo5aeM24LpDh67KELgMzKrMpCMV8G2NwLiT6mEpN7VNDrfVQdZsFNbAZRYbC2gga8WitSqKxKqykEg2IBGpB8ZzK0xXoVMLSVKpWiRTq0wVQi47B5K5GlxvrttOsmLMFBJIAG5OgECqq/aaxXqctKkUystVDdTfXs7MLaaEDfeefZauHwhoJeu7llUkWRA3jck5FHK5PKW6sCAQQQdiJGGLtX6lly5lLI175gLZvgRcfWB6mDXqEosSwVQt7kE253HwmylhwgIUtrzLFj982E2FzoJhRrpUF0dXF7XUgi/ygQ8LwpadQVWqPUYAhc1gFvvZVAF/fvLCU+G4g2IxlRKdVVp0GysosWqNlufeFW4253lxAREQE5/p33VifJ6izoJz/TvurE+T1FgfIIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiB9f6Cd1Ybz+q06Cc/0E7qw3n9Vp0EBI+Pwor0alFiQKilSRuLi0kRAo8FgB9sxYdn1ZKihXKggpl1A3N0MuK9BKqNTqKGRhYqdiJstEDCjRWmioihVUWAGwEzkGrxegjBWexNTq9v2rA/TUC+1yBM8TjQlRKYXM7i4GZR+JufkIGXEcGuIotSZmUNbVbXFjcWuPdNeHwIFia1aoRzZ/6LYfdNXHahFFFBIFStSptY27LVAGF/gSJ5xakyYU08MlmBWyU7A2zi9vleBZzGpUVFLMQqgXJOwEicYoVKmHdKXtkqRc22cE6/AGZ8Rwn2jD1aN8vWIVvvYkWvAkM4ALE6AXJmtcSppCqAxUrmAAuSCL7eM8FAtR6t2uSmViBblYkRTw2WiKSsRZAgYbiwtf4wMMFjRWz2VkZGysrWuDYHkSNiJDBqJj6dPrqjK1J6hDZbaMqgCyj94/SbuG8KGGVVFSo+X94gXJ3JsBcne5vImKrO+IwxWhVWpTchiVuhRhZu3t4H5bQLomwnN4zjD1GCX6mk1fL1p7LBVQOdD4nTW240nSyJ+jKJZWKBmR2dS2tmbc/3tAjPx/D9bSpK/WNU2yWYDW2vzP8Adpsp8awzVMi1LnOUvlbLmHLNbLfTxkitgKNQgvRpsQb3ZATMlwlMJ1YpoKf7gUZd77bQInHXthmHW9VmIXNcjc7ZhqoO1+Upkq0WOHHVGlhhUY1BqUNRVGTtbFTvfxAvrOpIi0Cjf7dV6wIKaKKl6NTPuthbsgG49q4Nt9LbyXxFGathAqsWWoWZh7KqFIa5997AflLKIGrE0BVQoSQD4bzHDYYU0yglh/mt/QCb4gUy9HKS1cyOyU861OrAWwZbbG11BsLgHX5y5iICIiAnP9O+6sT5PUWdBOf6d91YnyeosD5BERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQPr/QTurDef1WnQTn+gndWG8/qtOggIiICIiBFpcNoJUeotNc7tmYnXXxF9vlJOUXvbWexAj43B08QmSoCVuG0YqQVNwQQQRqIo4elRtlVVubX5k/HcmSJrrOwy2XNdgD7h4wNkREBERARErqCjENVLM65KhSyuQOzztAsYkL9F0/3qp/2r/nMv0bS/z/Oo/wCcCXMXqKvtMB8TaRTwugd6Yb/Vc/jPUwOGUXFKiB45V/GBk3EaA3r0h5x+c1jjGGO1ZG/0m/4SUlJV2VR8BM4EM8Tpcs5+FNz/AEnn6Up/u1f5L/lJsjYbGLUqVqa3vSYKx95UN+BEDEcQB2pVj/syPxmJx7csPXPlUfiwkt6iqLsQB4k2ni1VILBgVG5B00gVeK4piENkwpJtexY3+qqR981pxnE21wFUn/Kwt/8AK0kJiBVxqhamZFolhlbskl7a232mdLGPUxNSmCirSIBVgc7XW9xroNbbHYwNVbiGJVOsOGVFBGbPV7VudgoIv7ry1lfxY5uopf4lZb/BAah/5JYQE5/p33VifJ6izoJz/TvurE+T1FgfIIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiB9f6Cd1Ybz+q06Cc/0E7qw3n9Vp0EBETViavV03f91S30F4GwGezn+j1ZaODq4iq3ZeoajN4kqqn6sCAPhJj9IcKq02NTWo2VVAu972tlGt7i0C0iUmO6U4ajlAJqOxtkFgy23zA2t8N5jR44a2OajSNPqaWtRyb3ut+yQbb2+/bS4Xs04vErRXM1zqAAouSSbAATarAgEG4OoI5zlOkWNqksSjClh6gZlKlQyrbtdZsb3ICjx18IHUUKy1EV11VhcfORuJcQFDq1ADVKrZUDNlW9r6nkPvm7DLUA7ZTbRUGg+fP6CaeJ0KtRQtMUSDfN1qlhtpYDeBJoF8g6zLn55b2+V5lTfMoNit+R3Ej8Owf2ekKeYtqTe1gLm9gOQHISQikAAkt7zufpAylXwyqqfbCxCqtdiSdgMqky0nMYuqv2DiXaXMz1bC4uf2R+EC7qcTorTWpmLKzZVCglifDLvcWNxysZCbjl3qCmgYKmZAxKmsSuYZLi1uV77300ngGAWn1dStTqdrMS7hmLbX+PKeVaeBqFiQzhlykL1hUC1tANBodxrA2cP4uK/Um5XPmsVUlHtpYMQCCD4+BlZhcf9owT4JFbrhh2W5AsGXs/Ea7G2tjbaXOHxlCki06aVFRRYAUnsPumSY+lclaVW53IosCfjpAgcO45UerVWrh61OmAvVfqahLadq/Z01tpLLHrVejeg2SoLMobQNb9lvAGeHiB5YeufKo/FhH2yqdsLV8zIP8AqMClbo5ic6sMURcAtdntnuSxyhgDe4A8ABNr8Hxi1HNOtSs1ZaxJUjMQoUqQD7Ol95adbijtRpL/AKqp/ok9y4o/t0V8rN/UQKvG9Ha1dUFXF5yjZwGp9kkixBAIOXU21098l4DhbjDVMNiCjU2uqrTuLIRsSdZJOGxB3xNv9NMf1vPBgavPF1j8Fpj/AKIFRwzACljgtOs7qKTFvZGvW7Gw8b/SWHEsEjVFrVquVKZBUBQGBHLP7VieQtM6fA6QZ2LVSXN2PWFc3xy2vNy8Kw4/9pSfFhmP1NzAgYriFKpiMIyODlqkMLEe2jKNx4kfWXc1U8NTQWVFUXvooGs2wE5/p33VifJ6izoJz/TvurE+T1FgfIIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiB9f6Cd1Ybz+q06Cc/wBBO6sN5/VadBASHxRh1TUytVhUBT9WtyLjfwHzkyIHOHheHKZBwtitra9WD/z3vJeFTqFVaXD2QLqLGlva1759/fLiIHOcdx9VVQnCVQGbKxXKXIyk5QVJIubC/heY8N4A1Oml8Ph1cKuYszOCwFr5QALzpDMKeIRyVV1YjcBgSPjAiijijvXpj/TSP9XMicV4TWxGHqUziC2YaKUUAkagHSXM8vAquG4HEDD0lq4iorhAGAFM2IHjlMk/o9r64mufddB+CiTCbAnwkTC8Uo1nyU2YtlzewwsOR1HOA/Rqfv1v5r/nN2GwwpXsXNzftOW/HabogJo+xUr36qnf/SJviB4qAbAD4CexPCbbwPYlfxt1XDsXcILi16ppgm+xYa29w3lHRwmJ6qmWSu46s5Mj5XRy7HMQzA2y5QAb21EDrImrDZ+rTrLdZlGe22a2tvdebYCIiAiIgIiICIiAnP8ATvurE+T1Fl86kjQ299ryh6dd04jyeosD5BERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQPr/QTurDef1WnQTn+gndWG8/qtOggIiICIiBWcfpM9ABXpIM6ljVPYKg3IPj8Ocx6P1A9JmVUy5yqOqZBUUWs1vDcfK8sqtJXFnVWHgwuJnA0tiF6tqgYFQCbjXbf8JSdFsRUt1dTDvTcrmqu17l72N7i2upFidByl5hsOtJMiCwuT8ybn7zNsDRQxIqPVUAg02CnbW6htP96QaHCSMVVrVajVAwUKC1rWLEiwsCO0LXvtLKnSVb5QBc3PvJ5zOBFwVdqtIsbrdmCm1tAxCmx92s3WfMvaGUDUEak/Hl9JsiBgwbMCGso3Ft/DXlPWF9iRrymUQNdakHFiWH+lip+omOLwwq0mptfKwsbb2m6IGD0VbLmUHKbi4vY+6ZxEBERAREQEREBERAREQMajEC4F5Q9O+6sT5PVWXtVrC5Nh4iUXTvurE+T1FgfIIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiB9f6Cd1Ybz+q06Cc/wBBO6sN5/VadBAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREDF1B0OolD077qxPk9VZfOl+ZHwlD07H/wDJxHk9RYHyCIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgfX+gndWG8/qtOgnP9BO6sN5/VadBAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQERPG2PwgYNVtKHpy1+FYja3Y9RZd1KQa35Si6bLbhOIHhk9VZUfJIiJFIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiIH1/oJ3VhvP6rToIiAiIgIiICIiAiIgY1CQpKi5toL2v85B+1Yn+GH80flPYgefasT/AAo/mj8o+1Yn+FH80flPYgefasT/AAo/mj8o+1Yn+FH80flPYgefasT/AAo/mj8o+1Yn+FH80flPYgefasT/AAo/mj8o+1Yn+FH80flPYgefasT/AAo/mj8pOpklQWGU21F72+cRAyiIgIiICIiAnjbGIgYyg6dd1YnyeosRKPkUREgREQEREBERAREQEREBERA//9k=",
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/h8k0M1ubIxk\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x110ba9010>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-rv\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"h8k0M1ubIxk\", extras=['title=\"Random Variables\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-rv)\n",
+    "```{dropdown} 🎥 Random Variables\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/h8k0M1ubIxk\" title=\"Random Variables\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -628,50 +589,12 @@
     "```"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": "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",
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/OJZDPWSKIbw\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x110bb8690>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-event-5\n",
-    "YouTubeVideo(\"OJZDPWSKIbw\", extras=['title=\"Events Determined by S\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-event-5)\n",
+    "```{dropdown} 🎥 Events Determined by S\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/OJZDPWSKIbw\" title=\"Events Determined by S\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_03/02_Distributions.ipynb b/content/Chapter_03/02_Distributions.ipynb
index 0091ffaa..380bc8e0 100644
--- a/content/Chapter_03/02_Distributions.ipynb
+++ b/content/Chapter_03/02_Distributions.ipynb
@@ -149,49 +149,12 @@
     "five_rolls_sum"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/7ZznCAYa48Q\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10f49fc40>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-dist-rv\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"7ZznCAYa48Q\", extras=['title=\"Distribution of a Random Variable\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-dist-rv)\n",
+    "```{dropdown} 🎥 Distribution of a Random Variable\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/7ZznCAYa48Q\" title=\"Distribution of a Random Variable\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -502,48 +465,12 @@
     "- The statement of the Central Limit Theorem in Data 8 said that the distribution of the sum of a *large* random sample is roughly normal. But here you're seeing a bell shaped distribution for the sum of only five rolls. If you start out with a uniform distribution (which is the distribution of a single roll), then you don't need a large sample before the probability distribution of the sum starts to look normal."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/jOLQGfccbhs\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10f4c85b0>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-prob-hist\n",
-    "YouTubeVideo(\"jOLQGfccbhs\", extras=['title=\"Probability Histogram\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-prob-hist)\n",
+    "```{dropdown} 🎥 Probability Histogram\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/jOLQGfccbhs\" title=\"Probability Histogram\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -684,48 +611,12 @@
     "dist_S.event(np.arange(14, 22, 1));"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/QiTc-HKnlFc\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10f4c8370>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-notation-calc\n",
-    "YouTubeVideo(\"QiTc-HKnlFc\", extras=['title=\"Notation and Calculation\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-notation-calc)\n",
+    "```{dropdown} 🎥 Notation and Calculation\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/QiTc-HKnlFc\" title=\"Notation and Calculation\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_03/03_Equality.ipynb b/content/Chapter_03/03_Equality.ipynb
index 21b087b2..9e6086c4 100644
--- a/content/Chapter_03/03_Equality.ipynb
+++ b/content/Chapter_03/03_Equality.ipynb
@@ -60,49 +60,12 @@
     "We write this simply as $N_H = M$. Equivalently, $N_H = 3 - N_T$."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/Z-_RSmBktHM\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1102cfd90>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-two-eq\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"Z-_RSmBktHM\", extras=['title=\"Two Kinds of Equality\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-two-eq)\n",
+    "```{dropdown} 🎥 Two Kinds of Equality\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/Z-_RSmBktHM\" title=\"Two Kinds of Equality\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_04/01_Joint_Distributions.ipynb b/content/Chapter_04/01_Joint_Distributions.ipynb
index b7a68e75..08f8c635 100644
--- a/content/Chapter_04/01_Joint_Distributions.ipynb
+++ b/content/Chapter_04/01_Joint_Distributions.ipynb
@@ -73,49 +73,12 @@
     "The constraints on $x$ and $y$ are that each must be in the range $\\{0, 1, 2\\}$ and $\\vert x - y \\vert < 2$. "
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/5utr7G2OXXA\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10417fd30>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-joint-dist\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"5utr7G2OXXA\", extras=['title=\"Joint Distribution\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-joint-dist)\n",
+    "```{dropdown} 🎥 Joint Distribution\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/5utr7G2OXXA\" title=\"Joint Distribution\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -296,48 +259,12 @@
     "The table contains complete information about the relation between $X$ and $Y$. To find the probabiilty of any event determined by $X$ and $Y$, simply identify the cells that make the event happen, and add up their chances. This is an application of the fundamental method of finding probabilities by partitioning an event."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/ePADXFKYmh4\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1041a84c0>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-event-prob\n",
-    "YouTubeVideo(\"ePADXFKYmh4\", extras=['title=\"Events and Probabilities\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-event-prob)\n",
+    "```{dropdown} 🎥 Events and Probabilities\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/ePADXFKYmh4\" title=\"Events and Probabilities\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_04/03_Marginal_Distributions.ipynb b/content/Chapter_04/03_Marginal_Distributions.ipynb
index a514d0d9..f1e53d82 100644
--- a/content/Chapter_04/03_Marginal_Distributions.ipynb
+++ b/content/Chapter_04/03_Marginal_Distributions.ipynb
@@ -72,49 +72,12 @@
     "Everything, of course. Let's see how."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/E-m1o0bxFzo\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10688bcd0>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-marginal-dist\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"E-m1o0bxFzo\", extras=['title=\"Marginal Distribution\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-marginal-dist)\n",
+    "```{dropdown} 🎥 Marginal Distribution\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/E-m1o0bxFzo\" title=\"Marginal Distribution\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_04/04_Conditional_Distributions.ipynb b/content/Chapter_04/04_Conditional_Distributions.ipynb
index ca1800d3..f33f40af 100644
--- a/content/Chapter_04/04_Conditional_Distributions.ipynb
+++ b/content/Chapter_04/04_Conditional_Distributions.ipynb
@@ -70,49 +70,12 @@
     "To understand the relation between two variables we must examine the conditional behavior of each of them given the value of the other. Towards this goal, we will start by examining the example of the previous section and then develop the general theory."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/CnFSQPrm8Js\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x111c05cd0>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-cd-ex\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"CnFSQPrm8Js\", extras=['title=\"Conditional Distribution: Example\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-cd-ex)\n",
+    "```{dropdown} 🎥 Conditional Distribution: Example\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/CnFSQPrm8Js\" title=\"Conditional Distribution: Example\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -535,49 +498,12 @@
     "We can now generalize the calculations we did in the example above."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/8Ihsh1B0Eg4\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x111c2e4c0>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-cd-notation-calc\n",
-    "from IPython.display import YouTubeVideo\n",
-    "vid_cd_notation_calc = YouTubeVideo(\"8Ihsh1B0Eg4\", extras=['title=\"Conditional Distribution: Notation and Calculation\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-cd-notation-calc)\n",
+    "```{dropdown} 🎥 Conditional Distribution: Notation and Calculation\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/8Ihsh1B0Eg4\" title=\"Conditional Distribution: Notation and Calculation\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_04/05_Dependence_and_Independence.ipynb b/content/Chapter_04/05_Dependence_and_Independence.ipynb
index 0c8e7cd1..b6385d31 100644
--- a/content/Chapter_04/05_Dependence_and_Independence.ipynb
+++ b/content/Chapter_04/05_Dependence_and_Independence.ipynb
@@ -786,49 +786,12 @@
     "dist3.conditional_dist('Y', 'X')"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/zjKrjQJzqQ4\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10d454d30>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-ind-def\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"zjKrjQJzqQ4\", extras=['title=\"Independence: Formal Definition\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-ind-def)\n",
+    "```{dropdown} 🎥 Independence: Formal Definition\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/zjKrjQJzqQ4\" title=\"Independence: Formal Definition\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_05/01_Bounding_the_Chance_of_a_Union.ipynb b/content/Chapter_05/01_Bounding_the_Chance_of_a_Union.ipynb
index 33d3f184..d0c6d7ce 100644
--- a/content/Chapter_05/01_Bounding_the_Chance_of_a_Union.ipynb
+++ b/content/Chapter_05/01_Bounding_the_Chance_of_a_Union.ipynb
@@ -140,51 +140,12 @@
     "- If the chance of a union is at most $p$, then the chance of its complement is at least $1-p$."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": "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",
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/VqozXmsKzLE\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x107f71010>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"VqozXmsKzLE\", extras=['title=\"Bounds on the chance of a union\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid)\n",
+    "```{dropdown} 🎥 Bounds on the chance of a union\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/VqozXmsKzLE\" title=\"Bounds on the chance of a union\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_05/02_Inclusion_Exclusion.ipynb b/content/Chapter_05/02_Inclusion_Exclusion.ipynb
index 8878c609..6debd3a7 100644
--- a/content/Chapter_05/02_Inclusion_Exclusion.ipynb
+++ b/content/Chapter_05/02_Inclusion_Exclusion.ipynb
@@ -29,49 +29,12 @@
     "$$"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/HdLtwQ1R4m8\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1042e9ca0>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-ie-3e\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"HdLtwQ1R4m8\", extras=['title=\"Inclusion-Exclusion: Three Events\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-ie-3e)\n",
+    "```{dropdown} 🎥 Inclusion-Exclusion: Three Events\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/HdLtwQ1R4m8\" title=\"Inclusion-Exclusion: Three Events\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -93,48 +56,12 @@
     "$$"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/Y5CeWFc3l-c\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x104315460>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-ie-formula\n",
-    "YouTubeVideo(\"Y5CeWFc3l-c\", extras=['title=\"Inclusion-Exclusion Formula\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-ie-formula)\n",
+    "```{dropdown} 🎥 Inclusion-Exclusion Formula\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/Y5CeWFc3l-c\" title=\"Inclusion-Exclusion Formula\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -183,48 +110,12 @@
     "To end the section we will count the number of terms in each of the sums in the inclusion-exclusion formula, so we know the extent of the work that has to be done to apply it."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/vz3BPaI0GHQ\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x104315700>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-num-terms\n",
-    "YouTubeVideo(\"vz3BPaI0GHQ\", extras=['title=\"Number of Terms\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-num-terms)\n",
+    "```{dropdown} 🎥 Number of Terms\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/vz3BPaI0GHQ\" title=\"Number of Terms\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_05/03_The_Matching_Problem.ipynb b/content/Chapter_05/03_The_Matching_Problem.ipynb
index ec3321f2..ff7124b8 100644
--- a/content/Chapter_05/03_The_Matching_Problem.ipynb
+++ b/content/Chapter_05/03_The_Matching_Problem.ipynb
@@ -37,49 +37,12 @@
     "Consider a random permutation of $n$ elements which for simplicity we will call $\\{1, 2, \\ldots , n\\}$. For any $i$ in the range 1 through $n$, what is the chance that Position $i$ is a fixed point? In other words, what is the chance that letter $i$ falls in envelope $i$?"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/wM-DD_j2npE\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x29ae2ba9978>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-fix-loc\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"wM-DD_j2npE\", extras=['title=\"Fixed Locations\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-fix-loc)\n",
+    "```{dropdown} 🎥 Fixed Locations\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/wM-DD_j2npE\" title=\"Fixed Locations\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -131,48 +94,12 @@
     "If letters falling in the right envelopes are good events, then the worst possible event is every letter falling in a wrong envelope. That is the event that there are no matches, and is called a *derangement*. Let's find the chance of a derangement."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/PYp39ZsLxOg\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10e094070>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-derangement\n",
-    "YouTubeVideo(\"PYp39ZsLxOg\", extras=['title=\"Derangement\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-derangement)\n",
+    "```{dropdown} 🎥 Derangement\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/PYp39ZsLxOg\" title=\"Derangement\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_05/04_Sampling_Without_Replacement.ipynb b/content/Chapter_05/04_Sampling_Without_Replacement.ipynb
index 555fdbbf..a8484cba 100644
--- a/content/Chapter_05/04_Sampling_Without_Replacement.ipynb
+++ b/content/Chapter_05/04_Sampling_Without_Replacement.ipynb
@@ -273,50 +273,12 @@
     "```"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/bKtovlFnwxY\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x103a4dca0>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-count-cat\n",
-    "#| echo: false\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"bKtovlFnwxY\", extras=['title=\"Counting Categories\"'])\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-count-cat)\n",
+    "```{dropdown} 🎥 Counting Categories\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/bKtovlFnwxY\" title=\"Counting Categories\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   }
diff --git a/content/Chapter_06/01_Binomial_Distribution.ipynb b/content/Chapter_06/01_Binomial_Distribution.ipynb
index 288680b7..0ee6ff57 100644
--- a/content/Chapter_06/01_Binomial_Distribution.ipynb
+++ b/content/Chapter_06/01_Binomial_Distribution.ipynb
@@ -96,49 +96,12 @@
     "Parameters of a distribution are constants associated with it. The Bernoulli $(p)$ distribution has parameter $p$. The binomial distribution defined above has parameters $n$ and $p$ and is referred to as the binomial $(n, p)$ distribution for short. You should check that the Bernoulli $(p)$ distribution is the same as the binomial $(1, p)$ distribution."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/K2Rsnx8YbJQ\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10b061c70>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-binom-dist\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"K2Rsnx8YbJQ\", extras=['title=\"Binomial Distribution\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-binom-dist)\n",
+    "```{dropdown} 🎥 Binomial Distribution\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/K2Rsnx8YbJQ\" title=\"Binomial Distribution\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -336,48 +299,12 @@
     "sum(stats.binom.pmf([2, 3, 4], 7, 1/6))"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/MoBXF8icYkM\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10b0896a0>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-cdf\n",
-    "YouTubeVideo('MoBXF8icYkM', extras=['title=\"CDF\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-cdf)\n",
+    "```{dropdown} 🎥 CDF\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/MoBXF8icYkM\" title=\"CDF\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_06/05_Odds_Ratios.ipynb b/content/Chapter_06/05_Odds_Ratios.ipynb
index 3e10e55b..112f2ead 100644
--- a/content/Chapter_06/05_Odds_Ratios.ipynb
+++ b/content/Chapter_06/05_Odds_Ratios.ipynb
@@ -45,49 +45,12 @@
     "Binomial $(n, p)$ probabilities involve powers and factorials, both of which are difficult to compute when $n$ is large. This section is about simplifying the computation of the entire distribution. The result also helps us understand the shape of binomial histograms."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/-5suvosCTCE\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10383dd60>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-consec-odds\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"-5suvosCTCE\", extras=['title=\"Consecutive Odds\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-consec-odds)\n",
+    "```{dropdown} 🎥 Consecutive Odds\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/-5suvosCTCE\" title=\"Consecutive Odds\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -127,48 +90,12 @@
     "Even though we already have a formula for the binomial probabilities, building the distribution using consecutive ratios is better computationally and also helps us understand the shape of the distribution."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/F8lx8OmLsXE\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1038677f0>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-binom-co\n",
-    "YouTubeVideo(\"F8lx8OmLsXE\", extras=['title=\"Binomial Consecutive Odds\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-binom-co)\n",
+    "```{dropdown} 🎥 Binomial Consecutive Odds\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/F8lx8OmLsXE\" title=\"Binomial Consecutive Odds\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -247,48 +174,12 @@
     "That is why binomial histograms are either non-increasing or non-decreasing, or they go up and come down. But they can't look like waves on the seashore. They can't go up, come down, and go up again."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/--V_Z2QBc4w\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10383dc40>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-odds-mode\n",
-    "YouTubeVideo(\"--V_Z2QBc4w\", extras=['title=\"Odds and the Mode\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-odds-mode)\n",
+    "```{dropdown} 🎥 Odds and the Mode\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/--V_Z2QBc4w\" title=\"Odds and the Mode\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_06/06_Law_of_Small_Numbers.ipynb b/content/Chapter_06/06_Law_of_Small_Numbers.ipynb
index b2454c12..ba77c980 100644
--- a/content/Chapter_06/06_Law_of_Small_Numbers.ipynb
+++ b/content/Chapter_06/06_Law_of_Small_Numbers.ipynb
@@ -118,49 +118,12 @@
     "when $n$ is large, because $p_n \\sim 0$ and $np_n \\sim \\mu$."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/TmKepZHdOg0\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10681b550>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-recur-form\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"TmKepZHdOg0\", extras=['title=\"Recursive Formula\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-recur-form)\n",
+    "```{dropdown} 🎥 Recursive Formula\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/TmKepZHdOg0\" title=\"Recursive Formula\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -204,48 +167,12 @@
     "if $n$ is large, under all the additional conditions we have assumed. Here is a formal statement."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/5w1SnSOSTOo\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10681b910>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-poi-approx\n",
-    "YouTubeVideo(\"5w1SnSOSTOo\", extras=['title=\"Poisson Approximation\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-poi-approx)\n",
+    "```{dropdown} 🎥 Poisson Approximation\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/5w1SnSOSTOo\" title=\"Poisson Approximation\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_07/01_Poisson_Distribution.ipynb b/content/Chapter_07/01_Poisson_Distribution.ipynb
index 41477b87..aab93179 100644
--- a/content/Chapter_07/01_Poisson_Distribution.ipynb
+++ b/content/Chapter_07/01_Poisson_Distribution.ipynb
@@ -75,49 +75,12 @@
     "Here is our first infinite valued distribution."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/mc9r6cgXaHY\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10c8ffb20>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-poi-dist\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('mc9r6cgXaHY', extras=['title=\"Poisson Distribution\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-poi-dist)\n",
+    "```{dropdown} 🎥 Poisson Distribution\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/mc9r6cgXaHY\" title=\"Poisson Distribution\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -245,48 +208,12 @@
     "In later chapters we will learn a lot more about the parameter $\\mu$ of the Poisson distribution. For now, just keep in mind that the most likely value is essentially $\\mu$."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/BJ-hAV1TiHk\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10c923310>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-sum-poi\n",
-    "YouTubeVideo('BJ-hAV1TiHk', extras=['title=\"Sum of Independent Poissons\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-sum-poi)\n",
+    "```{dropdown} 🎥 Sum of Independent Poissons\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/BJ-hAV1TiHk\" title=\"Sum of Independent Poissons\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_07/02_Poissonizing_the_Binomial.ipynb b/content/Chapter_07/02_Poissonizing_the_Binomial.ipynb
index 05f06a9b..940b2814 100644
--- a/content/Chapter_07/02_Poissonizing_the_Binomial.ipynb
+++ b/content/Chapter_07/02_Poissonizing_the_Binomial.ipynb
@@ -53,49 +53,12 @@
     "However, something remarkable happens when the *number of trials is itself random and has a Poisson distribution.* After we see what happens, we will be able to understand why it matters."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/lnLUsr24r88\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1103f1c70>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-poi-trial\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('lnLUsr24r88', extras=['title=\"Poisson Number of Trials\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-poi-trial)\n",
+    "```{dropdown} 🎥 Poisson Number of Trials\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/lnLUsr24r88\" title=\"Poisson Number of Trials\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -121,48 +84,12 @@
     "We can sum the terms in this joint distribution appropriately to get the marginal distribution of $S$."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/JQT1NQNbxsQ\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x11041b640>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-poi-success\n",
-    "YouTubeVideo('JQT1NQNbxsQ', extras=['title=\"Poisson Number of Successes\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-poi-success)\n",
+    "```{dropdown} 🎥 Poisson Number of Successes\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/JQT1NQNbxsQ\" title=\"Poisson Number of Successes\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -230,48 +157,12 @@
     "```"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/2qLF6nNWPiw\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1103f1670>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-success-fail\n",
-    "YouTubeVideo('2qLF6nNWPiw', extras=['title=\"Successes and Failures\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-success-fail)\n",
+    "```{dropdown} 🎥 Successes and Failures\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/2qLF6nNWPiw\" title=\"Successes and Failures\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_08/01_Definition.ipynb b/content/Chapter_08/01_Definition.ipynb
index 017b9b37..9af806fb 100644
--- a/content/Chapter_08/01_Definition.ipynb
+++ b/content/Chapter_08/01_Definition.ipynb
@@ -40,49 +40,12 @@
     "from myst_nb import glue"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/jhKL5nVe0Mk\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x106666dc0>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-eq-def\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('jhKL5nVe0Mk', extras=['title=\"Two Equivalent Definitions\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}   🎥 See More\n",
-    "![](#vid-eq-def)\n",
+    "```{dropdown} 🎥 Two Equivalent Definitions\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/jhKL5nVe0Mk\" title=\"Two Equivalent Definitions\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -359,48 +322,12 @@
     "The similarity of the two histograms is because of the Law of Averages that you saw in Data 8 and that we will establish formally in this course."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/9gjBI8SMQis\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x106690850>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-exist\n",
-    "YouTubeVideo('9gjBI8SMQis', extras=['title=\"Existence\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}   🎥 See More\n",
-    "![](#vid-exist)\n",
+    "```{dropdown} 🎥 Existence\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/9gjBI8SMQis\" title=\"Existence\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_08/02_Applying_the_Definition.ipynb b/content/Chapter_08/02_Applying_the_Definition.ipynb
index 33f0d3a8..fc150db6 100644
--- a/content/Chapter_08/02_Applying_the_Definition.ipynb
+++ b/content/Chapter_08/02_Applying_the_Definition.ipynb
@@ -201,49 +201,12 @@
     "```"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/hq9GiEQ1ZzY\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x110ae1c40>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-exp-poi\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('hq9GiEQ1ZzY', extras=['title=\"Expectation: Poisson\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-exp-poi)\n",
+    "```{dropdown} 🎥 Expectation: Poisson\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/hq9GiEQ1ZzY\" title=\"Expectation: Poisson\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -322,48 +285,12 @@
     "```"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/ctcoolPNZjE\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x110b0a6d0>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-tail-sum\n",
-    "YouTubeVideo('ctcoolPNZjE', extras=['title=\"Tail Sum Formula\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-tail-sum)\n",
+    "```{dropdown} 🎥 Tail Sum Formula\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/ctcoolPNZjE\" title=\"Tail Sum Formula\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -434,48 +361,12 @@
     "$$"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/f2HhYnaGL2U\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x110b0a4f0>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-exp-geom\n",
-    "YouTubeVideo('f2HhYnaGL2U', extras=['title=\"Expectation: Geometric\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-exp-geom)\n",
+    "```{dropdown} 🎥 Expectation: Geometric\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/f2HhYnaGL2U\" title=\"Expectation: Geometric\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_08/03_Expectations_of_Functions.ipynb b/content/Chapter_08/03_Expectations_of_Functions.ipynb
index c9764b50..85faddbf 100644
--- a/content/Chapter_08/03_Expectations_of_Functions.ipynb
+++ b/content/Chapter_08/03_Expectations_of_Functions.ipynb
@@ -117,49 +117,12 @@
     "But expectation behaves differently under non-linear transformation."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/BWNa1Ri7eII\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10bd3f040>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-nonlinear-obs\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('BWNa1Ri7eII', extras=['title=\"Non-linear Function: Observation\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-nonlinear-obs)\n",
+    "```{dropdown} 🎥 Non-linear Function: Observation\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/BWNa1Ri7eII\" title=\"Non-linear Function: Observation\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -207,48 +170,12 @@
     "The crucial thing to note about this method is that **we didn't have to first find the distribution of $Y$**. That saves us a lot of work. "
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/jVBrCMCzO3o\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10bbfee20>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-nonlinear-calc\n",
-    "YouTubeVideo('jVBrCMCzO3o', extras=['title=\"Non-linear Function: Calculation\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-nonlinear-calc)\n",
+    "```{dropdown} 🎥 Non-linear Function: Calculation\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/jVBrCMCzO3o\" title=\"Non-linear Function: Calculation\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_08/04_Additivity.ipynb b/content/Chapter_08/04_Additivity.ipynb
index 5d8e1ffd..e1659d5d 100644
--- a/content/Chapter_08/04_Additivity.ipynb
+++ b/content/Chapter_08/04_Additivity.ipynb
@@ -86,49 +86,12 @@
     "And now note that **there are no assumptions about the relation between $X$ and $Y$**. They could be dependent or independent. Regardless, the expectation of the sum is the sum of the expectations. This makes the result powerful."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/HzZEhM4NHUQ\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1120e8c70>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-add-exp\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('HzZEhM4NHUQ', extras=['title=\"Additivity of Expectation\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-add-exp)\n",
+    "```{dropdown} 🎥 Additivity of Expectation\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/HzZEhM4NHUQ\" title=\"Additivity of Expectation\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -313,48 +276,12 @@
     "```"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/ruEpGZJwHmw\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x11206e850>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-ex-ue\n",
-    "YouTubeVideo('ruEpGZJwHmw', extras=['title=\"Example of an Unbiased Estimator\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-ex-ue)\n",
+    "```{dropdown} 🎥 Example of an Unbiased Estimator\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/ruEpGZJwHmw\" title=\"Example of an Unbiased Estimator\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_08/05_Method_of_Indicators.ipynb b/content/Chapter_08/05_Method_of_Indicators.ipynb
index 87e5a0c8..f7c55c24 100644
--- a/content/Chapter_08/05_Method_of_Indicators.ipynb
+++ b/content/Chapter_08/05_Method_of_Indicators.ipynb
@@ -71,49 +71,12 @@
     "It is important to note that the additivity works regardless of whether the trials are dependent or independent. "
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/hOIcQUYUNsM\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10cfe9ca0>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-method-ind\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('hOIcQUYUNsM', extras=['title=\"Method of Indicators\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-method-ind)\n",
+    "```{dropdown} 🎥 Method of Indicators\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/hOIcQUYUNsM\" title=\"Method of Indicators\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -174,48 +137,12 @@
     "Notice that we didn't use independence. Additivity of expectation works whether or not the random variables being added are independent. This will be very helpful in the next example."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/lz-UuQqvUOE\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10d0980d0>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-exp-binom-hyp\n",
-    "YouTubeVideo('lz-UuQqvUOE', extras=['title=\"Expectation: Binomial and Hypergeometric\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-exp-binom-hyp)\n",
+    "```{dropdown} 🎥 Expectation: Binomial and Hypergeometric\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/lz-UuQqvUOE\" title=\"Expectation: Binomial and Hypergeometric\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -329,48 +256,12 @@
     "The four indicators aren't independent but that doesn't affect the additivity of expectation."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/mxj4Gr_QUCM\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10d098370>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-apply-moi\n",
-    "YouTubeVideo('mxj4Gr_QUCM', extras=['title=\"Applying the Method of Indicators\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-apply-moi)\n",
+    "```{dropdown} 🎥 Applying the Method of Indicators\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/mxj4Gr_QUCM\" title=\"Applying the Method of Indicators\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_09/01_Probability_by_Conditioning.ipynb b/content/Chapter_09/01_Probability_by_Conditioning.ipynb
index d8323887..ae0b50f7 100644
--- a/content/Chapter_09/01_Probability_by_Conditioning.ipynb
+++ b/content/Chapter_09/01_Probability_by_Conditioning.ipynb
@@ -48,49 +48,12 @@
     "The theory in this section isn't new. It's the old familiar multiplication rule. We are just going to use it in the context of processes indexed by time, in a method that we are going to call *conditioning on early moves*."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/BCx-DlphihU\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1061eac40>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-cond-em\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('BCx-DlphihU', extras=['title=\"Conditioning on Early Moves\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-cond-em)\n",
+    "```{dropdown} 🎥 Conditioning on Early Moves\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/BCx-DlphihU\" title=\"Conditioning on Early Moves\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_09/02_Expectation_by_Conditioning.ipynb b/content/Chapter_09/02_Expectation_by_Conditioning.ipynb
index 8ebffe99..95b74fae 100644
--- a/content/Chapter_09/02_Expectation_by_Conditioning.ipynb
+++ b/content/Chapter_09/02_Expectation_by_Conditioning.ipynb
@@ -48,49 +48,12 @@
     "We will start with a simple example to illustrate the ideas. "
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/GrM0Ve-a010\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10928acd0>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-exp-by-cond\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('GrM0Ve-a010', extras=['title=\"Expectation by Conditioning\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-exp-by-cond)\n",
+    "```{dropdown} 🎥 Expectation by Conditioning\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/GrM0Ve-a010\" title=\"Expectation by Conditioning\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -524,51 +487,12 @@
     "```"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": "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",
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/k0CaLXwRCz0\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10ae15010>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-cond-exp\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('k0CaLXwRCz0', extras=['title=\"Conditional Expectation\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-cond-exp)\n",
+    "```{dropdown} 🎥 Conditional Expectation\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/k0CaLXwRCz0\" title=\"Conditional Expectation\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_09/03_Expected_Waiting_Times.ipynb b/content/Chapter_09/03_Expected_Waiting_Times.ipynb
index 3bb337f0..5c471fd9 100644
--- a/content/Chapter_09/03_Expected_Waiting_Times.ipynb
+++ b/content/Chapter_09/03_Expected_Waiting_Times.ipynb
@@ -35,55 +35,12 @@
     "We know that $W_H$ has the geometric $(p)$ distribution on $1, 2, 3, \\ldots $. We derived its expectation earlier by using the Tail Sum Formula. Here is a quick way to derive $E(W_H)$ without using the formula for the probabilities."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": "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",
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/MxeioidxzTA\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x17fbfb440>"
-      ]
-     },
-     "metadata": {
-      "scrapbook": {
-       "mime_prefix": "",
-       "name": "vid_exp_geom_rev"
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#| label: vid-exp-geom-rev\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('MxeioidxzTA', extras=['title=\"Expectation of the Geometric, Revisited\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-exp-geom-rev)\n",
+    "```{dropdown} 🎥 Expectation of the Geometric, Revisited\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/MxeioidxzTA\" title=\"Expectation of the Geometric, Revisited\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -186,54 +143,12 @@
     "- With probability $q$, the toss after $W_H$ is a tail, so $V = 1 + W^*$ where $W^*$ is an identically distributed copy of $W_{HH}$."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": "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",
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/XivRI0ba2v8\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x28001c0b0>"
-      ]
-     },
-     "metadata": {
-      "scrapbook": {
-       "mime_prefix": "",
-       "name": "vid_tree_ans2"
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#| label: vid-tree-ans2\n",
-    "YouTubeVideo('XivRI0ba2v8', extras=['title=\"Tree diagram for Answer 2\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-tree-ans2)\n",
+    "```{dropdown} 🎥 Tree diagram for Answer 2\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/XivRI0ba2v8\" title=\"Tree diagram for Answer 2\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_10/01_Transitions.ipynb b/content/Chapter_10/01_Transitions.ipynb
index 2fc1ba55..8d516516 100644
--- a/content/Chapter_10/01_Transitions.ipynb
+++ b/content/Chapter_10/01_Transitions.ipynb
@@ -51,49 +51,12 @@
     "\\dots$ is the set of all integers. In this course we will restrict the state space to be discrete and typically finite."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/YVGKlWO9ANk\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10994cd30>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-markov-prop\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('YVGKlWO9ANk', extras=['title=\"Markov Property\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-markov-prop)\n",
+    "```{dropdown} 🎥 Markov Property\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/YVGKlWO9ANk\" title=\"Markov Property\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -184,48 +147,12 @@
     "$$"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/G_g-45W7yYk\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1098d4580>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-transactions\n",
-    "YouTubeVideo('G_g-45W7yYk', extras=['title=\"Transitions\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-transactions)\n",
+    "```{dropdown} 🎥 Transitions\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/G_g-45W7yYk\" title=\"Transitions\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -589,48 +516,12 @@
     "reflecting_walk.simulate_path(1, 10, plot_path=True)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/YQziLVkV_Ro\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10994ca00>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-nstep-tm\n",
-    "YouTubeVideo('YQziLVkV_Ro', extras=['title=\"n-Step Transition Matrix\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-nstep-tm)\n",
+    "```{dropdown} 🎥 n-Step Transition Matrix\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/YQziLVkV_Ro\" title=\"n-Step Transition Matrix\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_10/02_Deconstructing_Chains.ipynb b/content/Chapter_10/02_Deconstructing_Chains.ipynb
index ca2833af..a669137e 100644
--- a/content/Chapter_10/02_Deconstructing_Chains.ipynb
+++ b/content/Chapter_10/02_Deconstructing_Chains.ipynb
@@ -25,49 +25,12 @@
     "Let $S$ be a finite or countably infinite set of states. Any stochastic matrix with rows and columns indexed by $S$ is the transition matrix of some Markov chain with state space $S$. The transition behaviors of Markov chains are thus as varied as the matrices. It is helpful to set up terminology to discuss some of these behaviors."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/Cnosegp3IlI\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10d9c2400>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-irre\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('Cnosegp3IlI', extras=['title=\"Irreducibility\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-irre)\n",
+    "```{dropdown} 🎥 Irreducibility\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/Cnosegp3IlI\" title=\"Irreducibility\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -99,48 +62,12 @@
     "Then for any two states $i$ and $j$, there is a segment of the path that starts at $i$ and ends at $j$, and another segment that starts at $j$ and ends at $i$, and both segments have positive probability. So it is possible to get from any state to any other state."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/dtsukvJUscs\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10d9c27f0>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-aperiod\n",
-    "YouTubeVideo('dtsukvJUscs', extras=['title=\"Aperiodicity\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-aperiod)\n",
+    "```{dropdown} 🎥 Aperiodicity\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/dtsukvJUscs\" title=\"Aperiodicity\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_10/03_Long_Run_Behavior.ipynb b/content/Chapter_10/03_Long_Run_Behavior.ipynb
index c171b32a..a8804726 100644
--- a/content/Chapter_10/03_Long_Run_Behavior.ipynb
+++ b/content/Chapter_10/03_Long_Run_Behavior.ipynb
@@ -88,49 +88,12 @@
     "Every irreducible and aperiodic Markov Chain on a finite state space exhibits astonishing regularity after it has run for a while. The proof of the convergence theorem below is beyond the scope of this course, but in examples you have seen the result by computation. All the results are true in greater generality for some classes of Markov Chains on infinitely many states. "
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/5ulR-5VcgLQ\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x111b56cd0>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-big-thm\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('5ulR-5VcgLQ', extras=['title=\"The Big Theorem\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-big-thm)\n",
+    "```{dropdown} 🎥 The Big Theorem\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/5ulR-5VcgLQ\" title=\"The Big Theorem\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -171,48 +134,12 @@
     "We will assume that the convergence theorem is true; you have observed in numerically in examples. The other properties follow rather easily. In the remainder of this section we will establish the properties and see how they are used."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/Qxg5EAP90dw\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x111b7d730>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-balance-eq\n",
-    "YouTubeVideo('Qxg5EAP90dw', extras=['title=\"Balance Equations\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-balance-eq)\n",
+    "```{dropdown} 🎥 Balance Equations\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/Qxg5EAP90dw\" title=\"Balance Equations\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -507,48 +434,12 @@
     "$$"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/LIXffVd1wic\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x111b7d850>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-steady-state\n",
-    "YouTubeVideo('LIXffVd1wic', extras=['title=\"Steady State\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-steady-state)\n",
+    "```{dropdown} 🎥 Steady State\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/LIXffVd1wic\" title=\"Steady State\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_11/01_Balance_and_Detailed_Balance.ipynb b/content/Chapter_11/01_Balance_and_Detailed_Balance.ipynb
index 9d81f419..aa973192 100644
--- a/content/Chapter_11/01_Balance_and_Detailed_Balance.ipynb
+++ b/content/Chapter_11/01_Balance_and_Detailed_Balance.ipynb
@@ -27,49 +27,12 @@
     "In this section we will see where the term *balance* comes from, and identify a condition under which it is easy to solve the balance equations."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/SfH-oJ-PTC4\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1096bbd00>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-bal\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('SfH-oJ-PTC4', extras=['title=\"Balance\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-bal)\n",
+    "```{dropdown} 🎥 Balance\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/SfH-oJ-PTC4\" title=\"Balance\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -120,48 +83,12 @@
     "Detailed balance turns out to be a stronger condition than balance. "
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/KEOTepEQCrU\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1096e2730>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-detailed-balance\n",
-    "YouTubeVideo('KEOTepEQCrU', extras=['title=\"Detailed Balance\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-detailed-balance)\n",
+    "```{dropdown} 🎥 Detailed Balance\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/KEOTepEQCrU\" title=\"Detailed Balance\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -198,48 +125,12 @@
     "But there is an important class of Markov chains for which it is easy to see that the detailed balance equations must have a solution. Therefore for those chains we have an easy way at finding the stationary distribution."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/CZNtQV5JpsU\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1096bbbe0>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-bod-chain\n",
-    "YouTubeVideo('CZNtQV5JpsU', extras=['title=\"Birth-and-Death Chains\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-bod-chain)\n",
+    "```{dropdown} 🎥 Birth-and-Death Chains\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/CZNtQV5JpsU\" title=\"Birth-and-Death Chains\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_11/02_Code_Breaking.ipynb b/content/Chapter_11/02_Code_Breaking.ipynb
index 4898bc86..e5e6e108 100644
--- a/content/Chapter_11/02_Code_Breaking.ipynb
+++ b/content/Chapter_11/02_Code_Breaking.ipynb
@@ -141,49 +141,12 @@
     "We can use Markov chains that have detailed balance to solve a class of problems that are intractable by other methods and are apparently unrelated to Markov chains. In this section we present an example of how such problems arise. In the next section we discuss a solution."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/C18hs5sHf0Y\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10aa47cd0>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-assum-nc\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('C18hs5sHf0Y', extras=['title=\"Assumptions\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-assum-nc)\n",
+    "```{dropdown} 🎥 Assumptions\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/C18hs5sHf0Y\" title=\"Assumptions\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -203,48 +166,12 @@
     "So we need data about which sequences of letters are common. Such data are now increasingly easy to gather; see for example this [web page](http://norvig.com/ngrams/) by [Peter Norvig](http://norvig.com), a Director of Research at Google. "
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/MUdZWq3b0mg\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10aa6f3d0>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-decoders\n",
-    "YouTubeVideo('MUdZWq3b0mg', extras=['title=\"Decoders\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-decoders)\n",
+    "```{dropdown} 🎥 Decoders\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/MUdZWq3b0mg\" title=\"Decoders\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -333,48 +260,12 @@
     "decoding"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/OrLjMVjpryY\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10aa47160>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-bigram\n",
-    "YouTubeVideo('OrLjMVjpryY', extras=['title=\"Bigrams\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-bigram)\n",
+    "```{dropdown} 🎥 Bigrams\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/OrLjMVjpryY\" title=\"Bigrams\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -459,48 +350,12 @@
     "bigrams"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/pi36R1lN8ww\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10aa6f850>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-score-decode\n",
-    "YouTubeVideo('pi36R1lN8ww', extras=['title=\"Score of a Decoder\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-score-decode)\n",
+    "```{dropdown} 🎥 Score of a Decoder\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/pi36R1lN8ww\" title=\"Score of a Decoder\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -565,48 +420,12 @@
     "bigrams.prob_of_path('t', ['a', 'd', 'a'])"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/zCAeQUaPf1E\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10aa47b20>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-best-decode\n",
-    "YouTubeVideo('zCAeQUaPf1E', extras=['title=\"The Best Decoder\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-best-decode)\n",
+    "```{dropdown} 🎥 The Best Decoder\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/zCAeQUaPf1E\" title=\"The Best Decoder\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -696,48 +515,12 @@
     "decoding.sort('Score of Decoder', descending=True)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/XLd2MuMIlR4\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10aa47910>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-larger-aplh\n",
-    "YouTubeVideo('XLd2MuMIlR4', extras=['title=\"Larger Alphabet\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-larger-aplh)\n",
+    "```{dropdown} 🎥 Larger Alphabet\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/XLd2MuMIlR4\" title=\"Larger Alphabet\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_11/03_Metropolis_Algorithm.ipynb b/content/Chapter_11/03_Metropolis_Algorithm.ipynb
index 3825fe1f..f8129148 100644
--- a/content/Chapter_11/03_Metropolis_Algorithm.ipynb
+++ b/content/Chapter_11/03_Metropolis_Algorithm.ipynb
@@ -25,49 +25,12 @@
     "The goal of Markov Chain Monte Carlo (MCMC) is to generate random samples from complicated high dimensional distributions about which we have incomplete information. For example, it might be that we don't know the normalizing constant of the distribution, as we saw in the code breaking example of the previous section."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/LwkIn66bkXc\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x105565cd0>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-metro-setup\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('LwkIn66bkXc', extras=['title=\"Metropolis: Setup\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-metro-setup)\n",
+    "```{dropdown} 🎥 Metropolis: Setup\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/LwkIn66bkXc\" title=\"Metropolis: Setup\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -137,48 +100,12 @@
     "The algorithm says that this steady state distribution is the same as the distribution $\\pi$ that was used to define the ratios $r(i, j)$."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/0_JsYu1NpBo\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1055908e0>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-metro-algo\n",
-    "YouTubeVideo('0_JsYu1NpBo', extras=['title=\"Metropolis Algorithm\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-metro-algo)\n",
+    "```{dropdown} 🎥 Metropolis Algorithm\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/0_JsYu1NpBo\" title=\"Metropolis Algorithm\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_12/01_Definition.ipynb b/content/Chapter_12/01_Definition.ipynb
index 613962c9..130ede20 100644
--- a/content/Chapter_12/01_Definition.ipynb
+++ b/content/Chapter_12/01_Definition.ipynb
@@ -58,49 +58,12 @@
     "Measuring the rough size of the squared deviations has the advantage that it avoids cancellation between positive and negative errors. The disadvantage is that squared deviations have units that are difficult to understand. The measure of spread that we are about to define takes care of this problem."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/9h34zgXmuqo\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1094946d0>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-sd\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('9h34zgXmuqo', extras=['title=\"Standard Deviation\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-sd)\n",
+    "```{dropdown} 🎥 Standard Deviation\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/9h34zgXmuqo\" title=\"Standard Deviation\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -433,48 +396,12 @@
     "Plots('-4X', dist4, '4X', dist3 )"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/zF-_2JwObjQ\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10946c2b0>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-linear-func\n",
-    "YouTubeVideo('zF-_2JwObjQ', extras=['title=\"SD: Linear Functions\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-linear-func)\n",
+    "```{dropdown} 🎥 SD: Linear Functions\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/zF-_2JwObjQ\" title=\"SD: Linear Functions\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -579,48 +506,12 @@
     "```"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/lfJWb_s3MSI\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10946c4f0>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-var-ind\n",
-    "YouTubeVideo('lfJWb_s3MSI', extras=['title=\"Variance of Indicator\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-var-ind)\n",
+    "```{dropdown} 🎥 Variance of Indicator\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/lfJWb_s3MSI\" title=\"Variance of Indicator\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_12/02_Prediction_and_Estimation.ipynb b/content/Chapter_12/02_Prediction_and_Estimation.ipynb
index bfb291f6..a2fe3994 100644
--- a/content/Chapter_12/02_Prediction_and_Estimation.ipynb
+++ b/content/Chapter_12/02_Prediction_and_Estimation.ipynb
@@ -59,49 +59,12 @@
     "$$"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/hnm1Ht5DiWk\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x105beed60>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-ls-cons-pred\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('hnm1Ht5DiWk', extras=['title=\"Least Squares Constant Predictor\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-ls-cons-pred)\n",
+    "```{dropdown} 🎥 Least Squares Constant Predictor\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/hnm1Ht5DiWk\" title=\"Least Squares Constant Predictor\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_12/03_Bounds.ipynb b/content/Chapter_12/03_Bounds.ipynb
index 608a1065..1b38f641 100644
--- a/content/Chapter_12/03_Bounds.ipynb
+++ b/content/Chapter_12/03_Bounds.ipynb
@@ -140,49 +140,12 @@
     "plt.title('$g(x) = x/c$,  $h(x) = I(x \\geq c)$');"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/WIokPScne_8\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10354b610>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-markov-ineq\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('WIokPScne_8', extras=['title=\"Markov's Inequality\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-markov-ineq)\n",
+    "```{dropdown} 🎥 Markov's Inequality\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/WIokPScne_8\" title=\"Markov's Inequality\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -333,48 +296,12 @@
     "Plot(dist2, event=np.append(np.arange(4), np.arange(15, 31, 1)), show_ev=True, show_sd=True)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/n6DilL4PzAQ\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x103521b20>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-chebyshev-ineq\n",
-    "YouTubeVideo('n6DilL4PzAQ', extras=['title=\"Chebyshev's Inequality\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-chebyshev-ineq)\n",
+    "```{dropdown} 🎥 Chebyshev's Inequality\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/n6DilL4PzAQ\" title=\"Chebyshev's Inequality\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -515,48 +442,12 @@
     "If you happen to know more about the distribution then of course you can do better than Chebyshev's bound. But in general Chebyshev's bound is as well as you can do without making further assumptions."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/y6RF-WBJciY\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1035ce1c0>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-std-unit\n",
-    "YouTubeVideo('y6RF-WBJciY', extras=['title=\"Standard Units\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-std-unit)\n",
+    "```{dropdown} 🎥 Standard Units\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/y6RF-WBJciY\" title=\"Standard Units\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_13/01_Covariance.ipynb b/content/Chapter_13/01_Covariance.ipynb
index 5079c5b5..e26fcb49 100644
--- a/content/Chapter_13/01_Covariance.ipynb
+++ b/content/Chapter_13/01_Covariance.ipynb
@@ -54,49 +54,12 @@
     "$$"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/0yvoSUYo2GY\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f8d40638850>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-cov-def\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('0yvoSUYo2GY', extras=['title=\"Covariance: Definition\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-cov-def)\n",
+    "```{dropdown} 🎥 Covariance: Definition\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/0yvoSUYo2GY\" title=\"Covariance: Definition\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -203,48 +166,12 @@
     "The focus of this chapter is utilizing covariance to find variances of sums. But covariance has a second important application, which we will study later in the course. Here is a preview."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/yJ580tGWdGg\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f8d40638820>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-cov-main-use\n",
-    "YouTubeVideo('yJ580tGWdGg', extras=['title=\"Covariance: Main Uses\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-cov-main-use)\n",
+    "```{dropdown} 🎥 Covariance: Main Uses\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/yJ580tGWdGg\" title=\"Covariance: Main Uses\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_13/02_Properties_of_Covariance.ipynb b/content/Chapter_13/02_Properties_of_Covariance.ipynb
index 01adf781..ac527609 100644
--- a/content/Chapter_13/02_Properties_of_Covariance.ipynb
+++ b/content/Chapter_13/02_Properties_of_Covariance.ipynb
@@ -116,49 +116,12 @@
     "This result simplifies proofs of facts about covariance, as you will see below. But as a computational tool, it is only useful when the distributions of $X$ and $Y$ are very simple – for example, when each has just a few possible values. In other calculations of covariance it is rarely a good idea to try to use this result. Rather, we will use the bilinearity property described at the end of this section."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/_FG4CxA6_9A\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7fa2f173bb80>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-uncorr-not-indep\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('_FG4CxA6_9A', extras=['title=\"Uncorrelated but Not Independent\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-uncorr-not-indep)\n",
+    "```{dropdown} 🎥 Uncorrelated but Not Independent\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/_FG4CxA6_9A\" title=\"Uncorrelated but Not Independent\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -212,48 +175,12 @@
     "To summarize, independent random variables are uncorrelated. But it is not true that uncorrelated random variables have to be independent."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/y8GIvXI7b-s\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7fa2f173b070>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-indep-imp-uncorr\n",
-    "YouTubeVideo('y8GIvXI7b-s', extras=['title=\"Independent Implies Uncorrelated\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-indep-imp-uncorr)\n",
+    "```{dropdown} 🎥 Independent Implies Uncorrelated\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/y8GIvXI7b-s\" title=\"Independent Implies Uncorrelated\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -292,48 +219,12 @@
     "```"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/Fs-rX3jdErU\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7fa2f173b580>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-bilinear-cov\n",
-    "YouTubeVideo('Fs-rX3jdErU', extras=['title=\"Bilinearity of Covariance\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-bilinear-cov)\n",
+    "```{dropdown} 🎥 Bilinearity of Covariance\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/Fs-rX3jdErU\" title=\"Bilinearity of Covariance\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_13/03_Sums_of_Independent_Variables.ipynb b/content/Chapter_13/03_Sums_of_Independent_Variables.ipynb
index 96ef20fe..1a41c3e4 100644
--- a/content/Chapter_13/03_Sums_of_Independent_Variables.ipynb
+++ b/content/Chapter_13/03_Sums_of_Independent_Variables.ipynb
@@ -47,49 +47,12 @@
     "After the dry, algebraic discussion of the previous section it is a relief to finally be able to compute some variances."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/M3Y_oBn2o7Y\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7fe8028979d0>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-var-sum\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('M3Y_oBn2o7Y', extras=['title=\"Variance of a Sum\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-var-sum)\n",
+    "```{dropdown} 🎥 Variance of a Sum\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/M3Y_oBn2o7Y\" title=\"Variance of a Sum\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -146,48 +109,12 @@
     "When the random variables are i.i.d., this simplifies even further."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/jHcdiO5N0ng\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7fe802897370>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-var-iid-sum\n",
-    "YouTubeVideo('jHcdiO5N0ng', extras=['title=\"Variance of IID Sample Sum\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-var-iid-sum)\n",
+    "```{dropdown} 🎥 Variance of IID Sample Sum\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/jHcdiO5N0ng\" title=\"Variance of IID Sample Sum\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -314,48 +241,12 @@
     "```"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/KoWHPWJBeJo\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7fe80296f430>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-binom-poi-var\n",
-    "YouTubeVideo('KoWHPWJBeJo', extras=['title=\"Binomial and Poisson Variance\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-binom-poi-var)\n",
+    "```{dropdown} 🎥 Binomial and Poisson Variance\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/KoWHPWJBeJo\" title=\"Binomial and Poisson Variance\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_13/04_Symmetry_and_Indicators.ipynb b/content/Chapter_13/04_Symmetry_and_Indicators.ipynb
index fa22b58b..33dfaa87 100644
--- a/content/Chapter_13/04_Symmetry_and_Indicators.ipynb
+++ b/content/Chapter_13/04_Symmetry_and_Indicators.ipynb
@@ -74,49 +74,12 @@
     "Before we apply the formula, let's start out by finding the covariance of two indicators. We will need this when we find the variance of a sum of indicators."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/9j8VwhEsWrk\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10a3e73d0>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-cov-ind2\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('9j8VwhEsWrk', extras=['title=\"Covariance of Two Indicators\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-cov-ind2)\n",
+    "```{dropdown} 🎥 Covariance of Two Indicators\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/9j8VwhEsWrk\" title=\"Covariance of Two Indicators\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -212,48 +175,12 @@
     "$$"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/XWHrPZOgD0A\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10a3bca90>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-vars-srs-sum\n",
-    "YouTubeVideo('XWHrPZOgD0A', extras=['title=\"Variance of a Simple Random Sample Sum\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-vars-srs-sum)\n",
+    "```{dropdown} 🎥 Variance of a Simple Random Sample Sum\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/XWHrPZOgD0A\" title=\"Variance of a Simple Random Sample Sum\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -307,48 +234,12 @@
     "Recall that when the sample is drawn with replacement, the variance of the sample sum is $n\\sigma^2$. When the sample is drawn without replacement, the formula is the same apart from the factor of $\\frac{N-n}{N-1}$. In the next section we will examine this factor."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/SJ2X0h2XdiY\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10a3e7670>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-var-hyp\n",
-    "YouTubeVideo('SJ2X0h2XdiY', extras=['title=\"Variance of the Hypergeometric\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-var-hyp)\n",
+    "```{dropdown} 🎥 Variance of the Hypergeometric\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/SJ2X0h2XdiY\" title=\"Variance of the Hypergeometric\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_14/01_Exact_Distribution_of_a_Sum.ipynb b/content/Chapter_14/01_Exact_Distribution_of_a_Sum.ipynb
index 25391d93..c5a76e9f 100644
--- a/content/Chapter_14/01_Exact_Distribution_of_a_Sum.ipynb
+++ b/content/Chapter_14/01_Exact_Distribution_of_a_Sum.ipynb
@@ -62,49 +62,12 @@
     "```"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/Z1TU6n3ZdSg\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x107b22640>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-pgf\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('Z1TU6n3ZdSg', extras=['title=\"Probability Generating Function\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-pgf)\n",
+    "```{dropdown} 🎥 Probability Generating Function\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/Z1TU6n3ZdSg\" title=\"Probability Generating Function\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -164,48 +127,12 @@
     "The result says that the pgf of the sum of two independent random variables is the product of the two pgfs. This extends easily to more than two random variables and yields a simple formula for the pgf of the sum of i.i.d. variables."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/86C9pL8lg1U\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x107af6520>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-pgf-sum\n",
-    "YouTubeVideo('86C9pL8lg1U', extras=['title=\"PGF of a Sum\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-pgf-sum)\n",
+    "```{dropdown} 🎥 PGF of a Sum\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/86C9pL8lg1U\" title=\"PGF of a Sum\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_14/02_PGFs_in_NumPy.ipynb b/content/Chapter_14/02_PGFs_in_NumPy.ipynb
index 6967d620..dc973f97 100644
--- a/content/Chapter_14/02_PGFs_in_NumPy.ipynb
+++ b/content/Chapter_14/02_PGFs_in_NumPy.ipynb
@@ -53,49 +53,12 @@
     "In this section we will use `NumPy` to carry out this algorithm."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/e3ItAqkAhj4\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10296c550>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-pgf-numpy\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('e3ItAqkAhj4', extras=['title=\"PGF in NumPy\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-pgf-numpy)\n",
+    "```{dropdown} 🎥 PGF in NumPy\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/e3ItAqkAhj4\" title=\"PGF in NumPy\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -211,48 +174,12 @@
     "The call to `print` displays the polynomial in retro typewriter style, using $x$ where we have been using $s$. Keep in mind that the final term is the coefficient of $x^0$."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/dJCAW5CRJlc\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10293f850>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-pgf-sum-iid\n",
-    "YouTubeVideo('dJCAW5CRJlc', extras=['title=\"PGF of a Sum of IID Random Variables\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-pgf-sum-iid)\n",
+    "```{dropdown} 🎥 PGF of a Sum of IID Random Variables\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/dJCAW5CRJlc\" title=\"PGF of a Sum of IID Random Variables\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_14/03_Central_Limit_Theorem.ipynb b/content/Chapter_14/03_Central_Limit_Theorem.ipynb
index 9166d04c..920e64c7 100644
--- a/content/Chapter_14/03_Central_Limit_Theorem.ipynb
+++ b/content/Chapter_14/03_Central_Limit_Theorem.ipynb
@@ -183,49 +183,12 @@
     "Now for the reason why the normal curve is important:"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/xf7-Cy4JWjE\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10c5c8520>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-clt\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('xf7-Cy4JWjE', extras=['title=\"Central Limit Theorem\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-clt)\n",
+    "```{dropdown} 🎥 Central Limit Theorem\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/xf7-Cy4JWjE\" title=\"Central Limit Theorem\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -334,48 +297,12 @@
     "```"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/ufuTZYneoJc\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10c5c88e0>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-prob-std-normal\n",
-    "YouTubeVideo('ufuTZYneoJc', extras=['title=\"Probabilities and the Standard Normal Curve\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-prob-std-normal)\n",
+    "```{dropdown} 🎥 Probabilities and the Standard Normal Curve\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/ufuTZYneoJc\" title=\"Probabilities and the Standard Normal Curve\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_14/05_The_Sample_Mean.ipynb b/content/Chapter_14/05_The_Sample_Mean.ipynb
index dbc0000d..b9e79a15 100644
--- a/content/Chapter_14/05_The_Sample_Mean.ipynb
+++ b/content/Chapter_14/05_The_Sample_Mean.ipynb
@@ -138,49 +138,12 @@
     "The *average* of the sample behaves differently."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/UjxM2o6nlbc\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x110ac6c10>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-iid-sample-mean\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('UjxM2o6nlbc', extras=['title=\"IID Sample Mean\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-iid-sample-mean)\n",
+    "```{dropdown} 🎥 IID Sample Mean\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/UjxM2o6nlbc\" title=\"IID Sample Mean\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -260,48 +223,12 @@
     "If you multiply the sample size by a factor, then the SD of the i.i.d. sample mean decreases by the square root of the factor."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/WowLqZku3X8\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x110af0400>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-wlln\n",
-    "YouTubeVideo('WowLqZku3X8', extras=['title=\"Weak Law of Large Numbers\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-wlln)\n",
+    "```{dropdown} 🎥 Weak Law of Large Numbers\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/WowLqZku3X8\" title=\"Weak Law of Large Numbers\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_14/06_Confidence_Intervals.ipynb b/content/Chapter_14/06_Confidence_Intervals.ipynb
index 2f4b4da0..653346bb 100644
--- a/content/Chapter_14/06_Confidence_Intervals.ipynb
+++ b/content/Chapter_14/06_Confidence_Intervals.ipynb
@@ -55,49 +55,12 @@
     "where $\\mu = E(X_1)$ and $\\sigma = SD(X_1)$ are fixed but possibly unknown parameters."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/-t8E1SW0wsg\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10ebab4f0>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-95ci-mean\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('-t8E1SW0wsg', extras=['title=\"95% Confidence Interval for Population Mean\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-95ci-mean)\n",
+    "```{dropdown} 🎥 95% Confidence Interval for Population Mean\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/-t8E1SW0wsg\" title=\"95% Confidence Interval for Population Mean\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -280,48 +243,12 @@
     "That's $z = 1.96$, which we have been calling $2$. It's good enough, but $z = 1.96$ is also commonly used for constructing 95% confidence intervals."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/K7juQV1jezw\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10eb80df0>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-ci-pop-mean\n",
-    "YouTubeVideo('K7juQV1jezw', extras=['title=\"Confidence Interval for Population Mean\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-ci-pop-mean)\n",
+    "```{dropdown} 🎥 Confidence Interval for Population Mean\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/K7juQV1jezw\" title=\"Confidence Interval for Population Mean\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -568,48 +495,12 @@
     "No bootstrapping required! "
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/iPnAPZQgjkA\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10ebab7c0>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-est-sd-mean\n",
-    "YouTubeVideo('iPnAPZQgjkA', extras=['title=\"Estimating the SD of the Sample Mean\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-est-sd-mean)\n",
+    "```{dropdown} 🎥 Estimating the SD of the Sample Mean\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/iPnAPZQgjkA\" title=\"Estimating the SD of the Sample Mean\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_15/01_Density_and_CDF.ipynb b/content/Chapter_15/01_Density_and_CDF.ipynb
index 31ea9b07..d1432c27 100644
--- a/content/Chapter_15/01_Density_and_CDF.ipynb
+++ b/content/Chapter_15/01_Density_and_CDF.ipynb
@@ -42,50 +42,13 @@
     "warnings.filterwarnings(\"ignore\")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/YU3ShHl3wE8\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x106c7ad30>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-intro-dense\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('YU3ShHl3wE8', extras=['title=\"Introduction to Density\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    ":::{dropdown} 🎥 See More\n",
-    "![](#vid-intro-dense)\n",
-    ":::"
+    "```{dropdown} 🎥 Introduction to Density\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/YU3ShHl3wE8\" title=\"Introduction to Density\" frameborder=\"0\" allowfullscreen></iframe>\n",
+    "```"
    ]
   },
   {
@@ -309,48 +272,12 @@
     "```"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/heahvMpBU_E\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x106ca2700>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-cdf-intro\n",
-    "YouTubeVideo('heahvMpBU_E', extras=['title=\"CDF\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-cdf-intro)\n",
+    "```{dropdown} 🎥 CDF\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/heahvMpBU_E\" title=\"CDF\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_15/02_The_Meaning_of_Density.ipynb b/content/Chapter_15/02_The_Meaning_of_Density.ipynb
index 926df3ed..dc2ab0c0 100644
--- a/content/Chapter_15/02_The_Meaning_of_Density.ipynb
+++ b/content/Chapter_15/02_The_Meaning_of_Density.ipynb
@@ -95,49 +95,12 @@
     "Being able to drop the equal sign like this is a major departure from calculations involving discrete random variables; $P(X = k)$ has disappeared. But it does have an analog if we think in terms of infinitesimals."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/7A7MqighkTA\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10ab4bca0>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-meaning-dense\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('7A7MqighkTA', extras=['title=\"Meaning of Density\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-meaning-dense)\n",
+    "```{dropdown} 🎥 Meaning of Density\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/7A7MqighkTA\" title=\"Meaning of Density\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_15/03_Expectation.ipynb b/content/Chapter_15/03_Expectation.ipynb
index 0c742dbd..79547f55 100644
--- a/content/Chapter_15/03_Expectation.ipynb
+++ b/content/Chapter_15/03_Expectation.ipynb
@@ -43,49 +43,12 @@
     "warnings.filterwarnings(\"ignore\")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/gJkb-5YXly4\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10b52a5e0>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-def-exp-cont\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('gJkb-5YXly4', extras=['title=\"Definition of Expectation\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-def-exp-cont)\n",
+    "```{dropdown} 🎥 Definition of Expectation\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/gJkb-5YXly4\" title=\"Definition of Expectation\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_15/04_Exponential_Distribution.ipynb b/content/Chapter_15/04_Exponential_Distribution.ipynb
index 0eee5689..f8b7703c 100644
--- a/content/Chapter_15/04_Exponential_Distribution.ipynb
+++ b/content/Chapter_15/04_Exponential_Distribution.ipynb
@@ -90,49 +90,12 @@
     "plt.title('Density of $T$');"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/D8DAKgt-NUk\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10dc26520>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-exp-dist\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('D8DAKgt-NUk', extras=['title=\"Exponential Distribution\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-exp-dist)\n",
+    "```{dropdown} 🎥 Exponential Distribution\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/D8DAKgt-NUk\" title=\"Exponential Distribution\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -389,48 +352,12 @@
     "```"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/x9ECxtFPrKE\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10dc268e0>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-mem-prop\n",
-    "YouTubeVideo('x9ECxtFPrKE', extras=['title=\"Memoryless Property\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-mem-prop)\n",
+    "```{dropdown} 🎥 Memoryless Property\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/x9ECxtFPrKE\" title=\"Memoryless Property\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_16/01_Linear_Transformations.ipynb b/content/Chapter_16/01_Linear_Transformations.ipynb
index 77c805a0..49d8f702 100644
--- a/content/Chapter_16/01_Linear_Transformations.ipynb
+++ b/content/Chapter_16/01_Linear_Transformations.ipynb
@@ -152,49 +152,12 @@
     "```"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/MA40E9QPCjU\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x111e99cd0>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid_linear_chvar\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('MA40E9QPCjU', extras=['title=\"Linear Change of Variable\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid_linear_chvar)\n",
+    "```{dropdown} 🎥 Linear Change of Variable\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/MA40E9QPCjU\" title=\"Linear Change of Variable\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_16/02_Monotone_Functions.ipynb b/content/Chapter_16/02_Monotone_Functions.ipynb
index 8405231d..bc029bb8 100644
--- a/content/Chapter_16/02_Monotone_Functions.ipynb
+++ b/content/Chapter_16/02_Monotone_Functions.ipynb
@@ -66,49 +66,12 @@
     "The method we developed in the previous section for finding the density of a linear function of a random variable can be extended to non-linear functions. "
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/1fKqhkOXddc\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10c4d8d60>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-cov-mono\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('1fKqhkOXddc', extras=['title=\"Change of Variable  Monotone Function\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-cov-mono)\n",
+    "```{dropdown} 🎥 Change of Variable  Monotone Function\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/1fKqhkOXddc\" title=\"Change of Variable  Monotone Function\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_17/01_Probabilities_and_Expectations.ipynb b/content/Chapter_17/01_Probabilities_and_Expectations.ipynb
index 4331abc4..2e1ca54b 100644
--- a/content/Chapter_17/01_Probabilities_and_Expectations.ipynb
+++ b/content/Chapter_17/01_Probabilities_and_Expectations.ipynb
@@ -41,49 +41,12 @@
     "init_printing()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/_DzYX2ia_zs\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1033c5c70>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-joint-dense\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('_DzYX2ia_zs', extras=['title=\"Joint Density\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-joint-dense)\n",
+    "```{dropdown} 🎥 Joint Density\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/_DzYX2ia_zs\" title=\"Joint Density\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -138,48 +101,12 @@
     "```"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/qfwy23iZBwk\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1033ee670>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-meaning-jd\n",
-    "YouTubeVideo('qfwy23iZBwk', extras=['title=\"Meaning of Joint Density\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-meaning-jd)\n",
+    "```{dropdown} 🎥 Meaning of Joint Density\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/qfwy23iZBwk\" title=\"Meaning of Joint Density\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -321,48 +248,12 @@
     "plt.title('Possible Values of $(X, Y)$');"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/IP7qhyvy6qE\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1033c59a0>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-tot-vol\n",
-    "YouTubeVideo('IP7qhyvy6qE', extras=['title=\"Total Volume\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-tot-vol)\n",
+    "```{dropdown} 🎥 Total Volume\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/IP7qhyvy6qE\" title=\"Total Volume\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -525,48 +416,12 @@
     "```"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/3Ri-wBDfSMI\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1033c5a30>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-prob-vol\n",
-    "YouTubeVideo('3Ri-wBDfSMI', extras=['title=\"Probabilities as Volumes\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-prob-vol)\n",
+    "```{dropdown} 🎥 Probabilities as Volumes\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/3Ri-wBDfSMI\" title=\"Probabilities as Volumes\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -845,48 +700,12 @@
     "Integral(f, (x, 0.25, y), (y, 0.5, 1)).doit()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/iWDyZg5Ckfo\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1033ee400>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-exp-joint\n",
-    "YouTubeVideo('iWDyZg5Ckfo', extras=['title=\"Expectation\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-exp-joint)\n",
+    "```{dropdown} 🎥 Expectation\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/iWDyZg5Ckfo\" title=\"Expectation\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_17/02_Independence.ipynb b/content/Chapter_17/02_Independence.ipynb
index 88cac381..40d3d6e2 100644
--- a/content/Chapter_17/02_Independence.ipynb
+++ b/content/Chapter_17/02_Independence.ipynb
@@ -79,49 +79,12 @@
     "```"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/jVHgjZBMYq0\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10c9e1c10>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-indep-joint\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('jVHgjZBMYq0', extras=['title=\"Independence\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-indep-joint)\n",
+    "```{dropdown} 🎥 Independence\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/jVHgjZBMYq0\" title=\"Independence\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_17/03_Marginal_and_Conditional_Densities.ipynb b/content/Chapter_17/03_Marginal_and_Conditional_Densities.ipynb
index a6fe76c9..c7e0d53c 100644
--- a/content/Chapter_17/03_Marginal_and_Conditional_Densities.ipynb
+++ b/content/Chapter_17/03_Marginal_and_Conditional_Densities.ipynb
@@ -41,49 +41,12 @@
     "init_printing()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/K4NlpU_Kd_0\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x107e3ad90>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-marg-dense\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('K4NlpU_Kd_0', extras=['title=\"Marginals\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-marg-dense)\n",
+    "```{dropdown} 🎥 Marginals\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/K4NlpU_Kd_0\" title=\"Marginals\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -411,48 +374,12 @@
     "plt.title('$f_Y$: Density of $Y$');"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/T0CkUU6V6CE\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x107e637f0>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-cond-dense\n",
-    "YouTubeVideo('T0CkUU6V6CE', extras=['title=\"Conditioning\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-cond-dense)\n",
+    "```{dropdown} 🎥 Conditioning\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/T0CkUU6V6CE\" title=\"Conditioning\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_17/04_Beta_Densities_with_Integer_Parameters.ipynb b/content/Chapter_17/04_Beta_Densities_with_Integer_Parameters.ipynb
index 2ec0a8f3..2a1908bc 100644
--- a/content/Chapter_17/04_Beta_Densities_with_Integer_Parameters.ipynb
+++ b/content/Chapter_17/04_Beta_Densities_with_Integer_Parameters.ipynb
@@ -159,51 +159,12 @@
     "In general for $1 \\le k \\le n$, the *$k$th order statistic* of $U_1, U_2, \\ldots, U_n$ is the $k$th value when the $U_i$'s are sorted in increasing order. This can also be thought of as the $k$th *ranked* value when the minimum has rank 1. It is denoted $U_{(k)}$."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEABALDBoYFhsaGRoeHRsfIiUlICAiIyglLSYnLic1MC8tLS01PVBCNThNOTAtRWFFS1NWW11bMkFlbWRYbVBZW1cBERISGRYZLxsbL1dCNzddV1dXV1dXV1ddV1dXV1dXWFdXV1dXV1dXV1dXV1dXV1dXV1dXV1dXV1dXV1dXV1dXV//AABEIAWgB4AMBIgACEQEDEQH/xAAbAAEAAgMBAQAAAAAAAAAAAAAAAgQDBQcBBv/EAEkQAAIBAgMECQICBQkGBgMAAAABAgMRBBIhBjFBUQUTIjRhcXORsjKBFKEVI7HB0TNCUlNyk9Lh8GJ0gpLC8SRUY4OioyU1RP/EABkBAQEBAQEBAAAAAAAAAAAAAAABAgMEBf/EACIRAQEAAgIDAQADAQEAAAAAAAABAhEDEgQhMRMUUWFBIv/aAAwDAQACEQMRAD8A5+AAAAAAAAAAAAAAAAdk6GhVeEwThJKH4andPnkVjjZ2zoDuGE9Cl8EBKNLEKH8pFzvG7e7Ru9tOOnuyeEhVWbrZqW61tLc/uZqdXNey7O5PnzFSVlfy/N2LoVKUMQnFOUHFWzN3cnpr4bxBYnOrypqF7tat2u9L+VvYy05STtLW7lr5fbcZbls0MeFjUSl1klJ5nlt/R4J/mZiNxcglcXMMazu7xaV7JmS40JXFyNxcCQuVqzkmrT0bta24ywulq7+O4ukZLi5XdS6zZrR4cb/65E5VcsM0tLK7S1/7kvpWW4uaPDdJ1aldZV2G7OPKN97fP/sbarUe6Ku+Pgv3vwM4ZTL4zMpWa4uYKc9bJ5la9+XgzFVxitJRvmWl7G+tq7XLi5hjWTi2ne2/Qx9dLPFSWVO65+Wo0bWri5hr1HGN1v3LzMdGq82VyzK172s1ruGvWza1cXIXI1qmWLft5kVluLmKnUu5K98r3/YncCZ6QTJXA9Kjw9b8Qp9c+ptrCy38t27iZ4Vk21rvVtHyMknZN+DAoOhic38qst/C9tfD/XgeypYnIlnWZN9rmraX+5dhK6T5pEJzl2rNdlX1W/8AMa2lm41XSUKywOL61p/+HqbufVu9uRx47V03K/R+JfPD1H702cVJrRJqAACgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAdr6A7jhPQpfBHFDt2z/AHDCehS+CAsqCTbSeu88qRuramdkWXYr5Xe7bdt10v3INmZmOcrIWiN/MX8/YxPEPkh178DHeM9om1dpu+nA9v5+xi698kOvfJDvDtGW/mL+fsYuvfJDr3yQ7w7RKpHNbhZ33EqmsWldXTV7bjH175Ide+SH6Q7R7TpWe9vkrWS8kMRTlNJRm4NST3Xvbhqede+SDrNq2gucqdo9o0owcml2pO8nxb8SUou907Pyvcwwm1uJ9e/ATOHaMkIqKsr/AMXzMU6Cc4z1Vt6tvPevfgOvfgX9DtGSWqtbQiovTM27btLfdkevfgOvfgT9IvaPa1JTte+n+vsSjFR3K32Ide/AfiHyRf0h2jLfzPJK6aa/IxSqya0eV80r/tPevfJE7w7ROjTyRsr+xkTMdKtd2ZnRrtv2s1/x4vv7Ek/P2JIkgqEVZvfqetX5mQAY4qytr+ZjqUcz3yV1Z24lkF2NV06rdH4pa6Yeqv8A62cVO37QdwxfoVfgziBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAO3bP9wwnoUvgjiJ27Z/uGE9Cl8EBfZFkmRYFXGY2nRUc7d5O0VGMpOTteySTb0RghjlVTy060VznSlBf/JIq9O5vxOAUGoydapZtZkv1UuF0WasqlNwUpSq9ZKzeWMVC0W76c7W1JfiUPD1st4ejC3CUuLZzmO3OTancXNhOEIq7jH2RUk8z0ilySSFxkLjpiBOVNrhZ7/MzYeEd8uO5E6pJtWBs8i5Iw1qsY6JJv9hbhpq46UgWnTqNXsvIwVI5WZ0zYgCUFdmdeB5PI8rDg1297bw47krAzVIcTXYjFyzNRdktNOJf5XH+c5J8reHBlnl1i2CthcS5PLJ3vuZS6c6TnSapUnllZOUuKvuSudePlx5Me0bniZ3k/ONsD5bB9NVqc1nm5wf1KVnpzT5m96WxzoUlKNnKTtG/DS97Hbjx/S6xb5fB5MM5h92uA+Th0riFLN1sm+T1XtuPpsFiVWpRqWte914p2Z35vGy4punk+FnwSZW7izS+pFxFOl9SLiOeHx5sPiaJIgiaNtpAAD0AAa/aDuGL9Cr8GcQO37QdwxfoVfgziAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA7ds/3DCehS+COInbtn+4YT0KXwQF9kWSZFgQlFO10tN3gYq/0syswYm9tOevkS/C/FZ6onCq07rRkEi7DCxt2lc5YzblJtXSc3q/dlunGMVvXncr4jDJK6S9j2hhYtXaXlY3JqtSaZMRXSi4xd5NW8vFlZK6st63Fmphlbs6GGhRzPXchl79F3tLPUlpu/IzUqKjq3diWHjbkVY0W55Sas+nuLzklvZRrTzSuWlho23FavSyvwLlvRlvSEJWZn8iskHJcr/c+X5vFw56vJdUw5OjLUlw4msxGFkpNxTaeunDwL7ta6KdfGWdkjc4OK8Mxl9T/AK3h5N472/swmHalmkrJbvFlDp7oydSSq01mdlGUVv03Nc95sMPi8zs95X6X6YjhbJRz1JK6V7JLm/4Hq8bx5Z04/e3bh8rkz5Znh9aTB9E1as0nCUI37UpK1l4X3s3/AExgnWpJQ+qDvFc1azX7DWYHafNNRrQUU3bNG+nmnwNx0hjYYem6k9VeyS3yfJHq/Lk4M569u/keRzfpjlZrXx8xDo+s5ZVSnfxi0vd6H1GBw3U0Y073a1fm3dnz8NrZ5u1Rjk4pSd7eb3n0lCtGpCNSDvGSun/rid/K5OTKSZTUY8vyuTlxkymoyqpGOsmopb23ZL7mWGOovdWpv/jj/EwODlomk3xazL24k68qSqU6XVQnOX1Ryx7MLPtvTddW8Ty4fHiwWliKf9OH/Mjz8fR/rqf/ADx/iY10dh1r1FFW49XDT8jU46EcXSq9XCMcLCE26iSXXNJ6Q/2L73xtZaG230cXdXWqZ6V+j1bD0V/6cPiiwB6AANftB3DF+hV+DOIHb9oO4Yv0KvwZxAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAdu2f7hhPQpfBHETt2z/cMJ6FL4IC+yLJMiwIMw1/pM0jFX+lkvwvxWi7O5sYyTVzWlmlh5Wve1znjXPGsuKnaNuZ7h5pxXgVa9Fx13rme0KLlrexd3a7u1yclFXK+EnvX3PKuHdr3uYqdNyegtuy27X9xVjVXWt8NxKWHdvquVlB3txLlaW1sba3KmLmm0lwJrDu31fYr1IOLsyZW6MrdIx/iiDViZQr9PUKcst5Sa0bjG6X34nh8jxf21ZdWOWWp9q8l2X4mprxak78W2jZ0MRCrHPCWaL4/ua4FbHY6hSdqmst+WKu/N8Dtw+LbhOPH3YznJcfrBhablPQ1O1mHkq6q2eScUr8muH7z6DBYulVT6p6rfFqzS/ge43F0qNO9ZrLLTLbNm8LHs8bt4+ete3fxeScV7T2+EoUJVZqEFeUnZI+o2pwkp4eDh2uqleXlltf3M/RvSeEnLJRiqcpcMijm8Lr9hsK1aNKMpzkoRiruT4Hp8jyMrnj/AOdaejl8mcllk+Ocp/5H3fQuGlSwtOE1aVm2uV3exraG0vR3W6RyO/8AKOkkvPTVH0F76p3T1TXFHPyeTKySzTlycnaPJRm1anJRnwcldLxtxM+EwsKEJPM232qlSb1k+cnwXhuXAwyrRppznJRjFXbZjjRni5KVaDhh004UZb6jW6VRcFxUfu+S8+HxnD4jFPHO7vHB8FueI8Xyp/Ly37HHpRw1aySSpT0XBZWZkUdopNYDEtO36qev2NtLmAX6il6cPijOeJW0XA9A9AAGv2g7hi/Qq/BnEDt+0HcMX6FX4M4gAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAO3bP9wwnoUvgjiJ27Z/uGE9Cl8EBfZFkmRYEGYa/wBLMb6Tw+bJ19LNutnje5kr/SyZfC/FeO9Gwmm12Wk9NWr/AJGuLNGVS2m7xOeN0543TNiX2Ge0dYqxUrOV+0e0HP8Aml7e17e1qkpKCzyUpJdqSWVN+V3Yx4Rqz8zHWc7a6LwMEJtPS9/2lt9ly9r1OM1KblJSi2siUbOKtqm766mODXWshOpUS1i0uejMCfEZUuSzUpVnXpyjUiqKjJTg46ylwafA8xjWnMlF1bbirNu7vvGV9GV9MGOzdRVy/Vkla2/dwPh0ffXKNbZ6jUk5qMo31ai9PZ7jnPby8vHc/ca/ZS96v9Ds+V7/AMDV43N11XNfNnle/mfX0MNClHJCOWK4ePNswY3o6jWeap2Zbsydr+fM9vh+Rjw5Xt/1nLhtwmM+x8/0Hf8AFU8vjfytqQ2szfio3vl6uOXlvd/zPpcFgaVFN01q98m7t+FzzH4OlWp2rJZY65r5cv3O3J5eOXNM5PXxvj4rjj1r4KN7rL9V1bzvp+Z9Bt25/haXLrO355dL/mbHozozCRlnoyVWUeOdSy+NkbGvQhVhKnUipQkrOL4nPm8nHLPGyfHXCdfrkh0jZbP+Ao577nlv/RzO35FajspgVU3udn/JuorflqzfpJJJJJLRJaJLkZ8nyMeTHWLpknTWqLiKlP6kW0ebD4uHxNGLH0FVoVab3ThKL+6MqK3S1Z08LXmt8ac2vY20ydGV+tw1Go986cJPzcUWTW7OQy4HDx35YKPtobID0AAa/aDuGL9Cr8GcQO37QdwxfoVfgziAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA7ds/wBwwnoUvgjiJ27Z/uGE9Cl8EBfZgxluqqX3ZJX9jOyFSKkmmrpqzXgB85DH4ijhaDlLDKc6UZRg4yje0E5OTzJJK+tlx0RZoYjEyqZKipqChCo5KEk3musiu3Zpq7fitDZzwtJuDdODdP8Ak24puH9l8Ny3chW3Ml+F+K0VqiXTWNq4ehnoUHXnmSyJ8HxIFmnitNUc8bpzxumSrrSTkrOyduT5FTpPFVMPgqlWjT6ypGN4x33d0r2W+2/7GStXzaWsj2jiMuj1RrtNr2m2v2U6Ur4zCyqYimoSUnFNJxUlbfZ+xehJwjWnGOaUYvLHnpe33ZkqYm6slYwwqOLuvuuY7TZcpt8fsltTjMRj40qrU6c8zkstslot3VtyvZa8z7GbyOq4q7gpSjHm7Xt7iE4RcpU6MITl9Ukkr+dtWRhJp3vre78S3KFscfxPSFarWdedSXWt3zXaa8uR1bonFTr4LD1av8pKHa8fH7lavs1gKlV1ZUO03dxUmot+Rs5S3JJKKVklokuSJllNGVmkqMbzSZsjVRlZp8i5HGRtrdEwsiY2QxkNE/sfM4uq51JX4NpLkkb+vXz+CNdicBnk5RaTe9Pdcss2ss2rdHVGqiS3S0a/efL7fY6bxEKF7U4wjK3OTvq/2H2eFwapvM3eXC25Gu2i2dhjlGSl1daKspNXTXJ/xHabXc25xgcZPD1Y1abalF304+D8GdP6bxMo0I5ez1j18rXsaHorYfJVjPEVIyjF3UIJ9prm3wPqsZh41oOEtOKa4Mmd3NRZlJY+RWh9X0dWdShCUtZapvnZ2uayPQEr61I5eaTv7G6pU4wjGMVaMVZHHGWOnNnjZqJdZGHam1GK3tuyRNdKYf8AroPy1/YeU/qRcR3w+OWHxV/StD+lJ/2adR/siKmPhUi4qjXqRkmmuqlFNPxnYupkkbaanZWT/BU1klGKzKGe2aULvLJrg7G4MGDqwlD9XpGMpRta1nGTT/NMzgegADX7QdwxfoVfgziB2/aDuGL9Cr8GcQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHbtn+4YT0KXwRxE7ds/3DCehS+CAvsiyTIsCDMNb6WZmYq25kvwvxVABwcAAADw9AA8MTpyvppq+O++49yz56+f8Aka6/6MoMWSf9Lhp5/wABGE+L4rjwvuHX/RlBilCea6ejeqTtZLcRcKjs7631/Pd+Rev+ozgxSpybvm1SVvNX/IZZ8/NeHC35k6z+1Zk7C+ljHSi0nfn+4mSmxs8PTwgAADydVwWZRzO6SV7Xbdlr9yxSxkZVp0lGV4JXk7ZdeF73v4NGF0lNZZXs+TafPRrcTodG0IPNGnHO3dzavJvnmetzrh8dcPieBryk60ZNS6uplUkrXWVSt5q9vsXUYcPRjTgoQVor9r1bfNt8TMjbSt0dhXRjOLaadSpNW5Tk5a/dsthAD0AAa/aDuGL9Cr8GcQO37QdwxfoVfgziAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA7ds/3DCehS+COInbtn+4YT0KXwQF9kWSZFgRZjnG6seLFU3UdJTi6i1cb6+33RjxGKy1IU4Rc6ktbXsoxW+UnwXBc392BF0GedSy1YWMdIz1ir1DHUstWKGH6SU8TXo5bRoqN5t8Wru64Kz/JjpDpGXqWOpfgRo9KYapKMYV6c5SbUVGSbbSuy5YvSHSKvUy8B1L8B0lWlSoTqRteGVu+to5lmf/Lcr1ulIwxkaUpwjTdLNmfGcpdlZty0TduI6Q6RY6ljqX4GajWhUScJKSd2rcUna/lfieUq0Z58ju4NxaelpWvZ+6HSHSMXUyMOIqxpypxm7OpLLBau7t+S8fFGHo2piYUqk8RTk5Ri5SirylOST0hra2lkklwKVCOJxFehialJOlTvaCThK8lq7Sauou2+zerS0V3SHSNz1LMeJkqUVKW5yjG64OTsm/C7RhoY3EddRp1aSpqUamd5lK8oq/Ztw8X7DEVfxdqVFXpZourV/m2jJPLD+k21a+5c+A6Q6xa6mQ6iXgWmhYnSHSKvUS8A6TWraSW9vgWrFPpijKeErwgm5SpySS3vTcvEdIdIwLER6h15XjTSzXkrNx4NLx4XM1BOcVK1r+Kf7G0Z5KNWk8mSUZLs5o5o+F1+4x9H4FUIyV7ylLNJpKKva1oxWiVkv83qXpDrHmaEJxU5wjKX0xcknLyT3kqmPpRqKm5rPomlrlu7RzNfTd6K+9lLE0YzrYjrZqFKKpZ92sbN2zP6Ve+4tU+iKSdW+aXWO7Tk9O1n7NtV2tee7kak01Jpkh0jSzRhKcY1G8uS93m4r/M1dfpauquKnRp56dGEV25uMbrM5SiknflfT6TaUaGHqwlKNOEoVHmk8v1tO13ffu3ksVg6GRyqQWWMbvellS3NLRq3BgWqV8qzNOVldx0Tfh4EyhXr1JYN1aa6up1anFOztpmyv9jLeErqrSp1I/TOMZLyauBlAAGv2g7hi/Qq/BnEDt+0HcMX6FX4M4gAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAO3bP9wwnoUvgjiJ27Z/uGE9Cl8EBfDPTwDR9GYeU6tRtxVKliazglvc22pOT4JOUtCeHzOOOnDWvnnFLissF1a9rP/iNwo23Iw/hUqvWxvGTVpW3TS3XXNcwNL0fjKqqRqVKdfqHSp04ScZO019Upw+pXbte3802PSuE/E4eVKE1HPa7T0lG+sdODWn3LGPw8qtCrTjNwlOEoqS/mtq1zSvoWtdSj1dNtUoRjBP9TGDUnaV9db8Nbq9gLVToyq8L1UKnVSuv505rKv5t04tfaxk6O6Fp4fWMpuUneo7u034x3WW5eBsxYDXU+iIRquqqlXrZaSnn+pXuo2tZJeFiWNnV6ynGi1e6c04XWS+rcr6aXSS1v4Jl+wsBFoqvo+mqNWjSiqUakZp5ElrJWzeZcsLAaCHQ1WVWjUlUqU7RUakITja8I2g46Xy6ydnzRsMDRn1tatNZc+WMYvflhezl4tt/axfsLARsGiVhYDX0ujf1jqVp9dLK4RTioxjF77R5vS7fLgXspKwsBGwsSsLARsLErCwEbCxKwsBqdo8NVrYWdKnlee0ZXi5OzduzZqzW+7vuJU1jXUlFumo9pKplVn2ey0szd72vfQ2lgB85hMHi6WBpKhJyqy0l1kksiu3eCtZO9t6e/iW10fiquFdGvWinKm4txV5NtWu5aK3gl9zc2PQK1alPqHCDjnyZU3fKna17b7eBPBYZUaNKlFtqnCME3vairXfsZj0AAANftB3DF+hV+DOIHb9oO4Yv0KvwZxAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAdv2e7hhPQpfBHEDt+z3cMJ6FL4IDYHh6APAengAAAAAAAAAAAAegDwHoA8AAAAAAAAAAAA9A8B6AAAAAADX7QdwxfoVfgziB2/aDuGL9Cr8GcQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHb9nu4YT0KXwRxA7fs93DCehS+CA2AAAAAADVTq1/xdWnCUFBU6co5k5Wu5J2Sa5czNkxHGtT+1J/4jnly443Vq6Xwa+VCu//AOlr+zTh++55+Ck/qr15f8Sh8UjH8jA02INd+jqfGVZ/+/V/xD9G0udX+/rf4ifycDTYg136Np86v9/W/wAR4+i6XHrH51qr/wCon8nBdNi2Vcb0nRoQcp1I6cM0bt3skrvmYV0ZQ401L+1eX7TzEdF0qlN01BQu4tOMUmnGSkn7pCeTLdaNPf05h80YuoruN1btecbxvqjJ+lKfCNZ+VGq/+kwUcJOTgpNfq605uVmm99klytLV3NoellR/Skf6rEf3FT+A/Skf6rEf3M/4F4AUf0nH+qr/ANzP+BXh03mq1IKhWtDIr5XfNJN6x4K1tbm2KeLw8XKM42jUcoJyvZuMZXt48V9wKuB6TrVIy/8ADSbjKUcylBRlZ71q3+3W5Y6/FPdh6a86z/dBl1Kx6BRlHFSTT6mN1bRzbXk9CXRUakaEIVrupBZHJu+bLopX36rXXXUuFHoupUkqyq3bjWqKN1bsZrwtzVmtQLwAAAAAAAAAA1+0HcMX6FX4M4gdv2h7hi/Qq/BnEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAB2/Z7uGE9Cl8EcQO37PdwwnoUvggNgAAAYAGjwfS0qrpTtScKz/Vwi26kY2veXB+O63ibrIQpYanCTlCEIyl9TjFJvza3mUxePG3di7RyIZUSA/PD+jaOVHuVHoHTH+jbywseg11iBWx2Np0IZqk4wve13vfgWSlieiqVWtCtUzScFZRu8r1um48ddfsuSKNNR6fdHD0ZzhnnVhTu3OMW6jtFpp7rS0fBH0VGeaKel7a5XdX8ys8BRdWbdCPbhacssbTu3pJcX58yxhsNTpRy0oQpx/owior2QGUAAY8RXjThKpN5YQTlJ8kt58tUrTxOJhjYYWtOFKnKNFXj2p/Ve2tk1pfmfWNXVnuK+MiskY3y3nC3ay7pJ2XPdu4gU6HTSdaUKsHRioRlF1GlKV20+zfTct+ptIyTV1uZruj+iI0a1Ws5dZOo/qa1Uczklfjvt5JaI2QAAAAAAAAAAAAABr9oe4Yv0KvwZxA7ftD3DF+hV+DOIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADt+z3cMJ6FL4I4gdt2fv+Awm7+QpcP9hAbIEe1zXsO1zXsBIEe1zXsO1zXsBIEe1zXsO1zXsBIEe1zXsO1zXsBIEe1zXsO1zXsBIEe1zXsO1zXsBIEe1zXsO1zXsBoKeG6R691M8ercnalUq7u1o+xDdbTLc+gje2u887XNew7XNewEgR7XNew7XNewEjUbTKlHCyrVXKPUWnCUGlKM9ytfTW9teZte1zXsY69BVIOFSMZxdrxkrp2d1p5ga7oChiFh6Eq1Zy/VxbhlXFX7Undt+VjbkUn4ew15r2AkCOvNew15r2AkCPa5r2Ha5r2AkCNpc17DXmvYCQI6817Dtc17ASBG0ua9hrzXsBR2h7hi/Qq/BnEDtu0F/wABi9V/IVeH+wziQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA7fs93DCehS+COIHb9nu4YT0KXwQGxAAAAAAAAAAAAAAAAAAAAAAAB4D0AeHoAAAAAAAAAAAAAABrtoe4Yv0KvwZxA7ftD3DF+hV+DOIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADt+z3cMJ6FL4I4gdv2e7hhPQpfBAbEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAa7aHuGL9Cr8GcQO37Q9wxfoVfgziAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA7fs93DCehS+COIHb9nu4YT0KXwQGxAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGu2h7hi/Qq/BnEDt+0PcMX6FX4M4gAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAO37PdwwnoUvgjiB2/Z7uGE9Cl8EBsQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABrtoe4Yv0KvwZxA7ftD3DF+hV+DOIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADt+z3cMJ6FL4I4gdv2e7hhPQpfBAbEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA8PSE4J79QKO0PcMX6FX4M4gdu2gX/wCPxf8Au9X4M4iAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAO37PdwwnoUvgjiB2/Z7uGE9Cl8EBsQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADHVbS7Ku7r9upkMdV2WssviBS2g7hi/Qq/BnEDt20HcMX6FX4M4iAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAO37PdwwnoUvgjiB2/Z7uGE9Cl8EBsQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADHUpqSs+dzIQm3wVwKO0HcMX6FX4M4gdu2g//AF+L/wB3q/BnEQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAB2/Z7uGE9Cl8EcQO37PdwwnoUvggNiAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHh6eAARm9DDUmk7Wvu3u28CvtD3DF+hV+DOIHa+nbPAYpr/AMvV+DOKAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADt+z3cMJ6FL4IADYgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAhPcQlBPn9gAKHTkbdH4pcqFW3l1bOKABQABAAAAAAAAAAAAAB//2Q==",
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/3AIyXF1kQPA\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1c16ba7a788>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-joint-os\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('3AIyXF1kQPA', extras=['title=\"Joint Density of Order Statistics\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-joint-os)\n",
+    "```{dropdown} 🎥 Joint Density of Order Statistics\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/3AIyXF1kQPA\" title=\"Joint Density of Order Statistics\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_18/01_Standard_Normal_Basics.ipynb b/content/Chapter_18/01_Standard_Normal_Basics.ipynb
index 195aec18..fe05a0f2 100644
--- a/content/Chapter_18/01_Standard_Normal_Basics.ipynb
+++ b/content/Chapter_18/01_Standard_Normal_Basics.ipynb
@@ -71,53 +71,12 @@
     "In fact there is a family of Rayleigh distributions, each of whose members has the distribution of $cV$ for some positive constant $c$. But let us define $V$ to have \"the\" Rayleigh distribution, and let's see what $V$ has to do with standard normal variables."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/8vLTUrqS67A\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x11012dc70>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-const-int\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('8vLTUrqS67A', extras=['title=\"Constant of Integration\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-const-int)\n",
+    "```{dropdown} 🎥 Constant of Integration\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/8vLTUrqS67A\" title=\"Constant of Integration\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -312,48 +271,12 @@
     "Therefore $E(X^2) = 1$. We know that $E(X) = 0$. So $Var(X) = 1$ and hence $SD(X) = 1$."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/190dwmZKKPk\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x11014da60>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-sn-basics\n",
-    "YouTubeVideo('190dwmZKKPk', extras=['title=\"Standard Normal Basics\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-sn-basics)\n",
+    "```{dropdown} 🎥 Standard Normal Basics\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/190dwmZKKPk\" title=\"Standard Normal Basics\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   }
diff --git a/content/Chapter_18/03_The_Gamma_Family.ipynb b/content/Chapter_18/03_The_Gamma_Family.ipynb
index bc95fc93..e1babf12 100644
--- a/content/Chapter_18/03_The_Gamma_Family.ipynb
+++ b/content/Chapter_18/03_The_Gamma_Family.ipynb
@@ -279,49 +279,12 @@
     "```"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/BQoIs4eWZa4\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1095b8c40>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-gamma-int-param\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('BQoIs4eWZa4', extras=['title=\"Gamma with Integer Shape Parameter\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-gamma-int-param)\n",
+    "```{dropdown} 🎥 Gamma with Integer Shape Parameter\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/BQoIs4eWZa4\" title=\"Gamma with Integer Shape Parameter\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_18/04_Chi_Squared_Distributions.ipynb b/content/Chapter_18/04_Chi_Squared_Distributions.ipynb
index fb4fe933..1ca046b8 100644
--- a/content/Chapter_18/04_Chi_Squared_Distributions.ipynb
+++ b/content/Chapter_18/04_Chi_Squared_Distributions.ipynb
@@ -64,49 +64,12 @@
     "That's the gamma $(1/2, 1/2)$ density. It is also called the *chi-squared density with 1 degree of freedom,* which we will abbreviate to chi-squared (1)."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/TXM-yzqYmwg\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1039c9c70>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-chi2\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('TXM-yzqYmwg', extras=['title=\"Chi-Squared Distributions\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-chi2)\n",
+    "```{dropdown} 🎥 Chi-Squared Distributions\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/TXM-yzqYmwg\" title=\"Chi-Squared Distributions\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_19/02_Moment_Generating_Functions.ipynb b/content/Chapter_19/02_Moment_Generating_Functions.ipynb
index 1331f454..f25dd17b 100644
--- a/content/Chapter_19/02_Moment_Generating_Functions.ipynb
+++ b/content/Chapter_19/02_Moment_Generating_Functions.ipynb
@@ -69,49 +69,12 @@
     "We will list the three ways first, and then use them all in examples."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/uljcfZRj7os\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x111a51c10>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-mgf\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('uljcfZRj7os', extras=['title=\"Moment Generating Functions\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-mgf)\n",
+    "```{dropdown} 🎥 Moment Generating Functions\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/uljcfZRj7os\" title=\"Moment Generating Functions\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -280,48 +243,12 @@
     "```"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/juRbr3EXnJg\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x111a7f430>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-mgf-tail\n",
-    "YouTubeVideo('juRbr3EXnJg', extras=['title=\"MGF and Tails\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-mgf-tail)\n",
+    "```{dropdown} 🎥 MGF and Tails\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/juRbr3EXnJg\" title=\"MGF and Tails\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_19/03_MGFs_Normal_and_the_CLT.ipynb b/content/Chapter_19/03_MGFs_Normal_and_the_CLT.ipynb
index 61714afb..5dc4241a 100644
--- a/content/Chapter_19/03_MGFs_Normal_and_the_CLT.ipynb
+++ b/content/Chapter_19/03_MGFs_Normal_and_the_CLT.ipynb
@@ -18,49 +18,12 @@
     "# MGFs, the Normal, and the CLT"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/SXyYS0ztJ6U\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10f5cacd0>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-normal-mgf\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('SXyYS0ztJ6U', extras=['title=\"Normal MGF\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-normal-mgf)\n",
+    "```{dropdown} 🎥 Normal MGF\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/SXyYS0ztJ6U\" title=\"Normal MGF\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_19/04_Chernoff_Bound.ipynb b/content/Chapter_19/04_Chernoff_Bound.ipynb
index 06300e6f..de15f371 100644
--- a/content/Chapter_19/04_Chernoff_Bound.ipynb
+++ b/content/Chapter_19/04_Chernoff_Bound.ipynb
@@ -73,49 +73,12 @@
     "Moment generating functions can help get good bounds on tail probabilities. In what follows, we will assume that the moment generating function of $X$ is finite over the whole real line. If it is finite only over a smaller interval around 0, the calculations of the mgf below should be confined to that interval."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/HCEQFjHyLIw\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1046ebd00>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-exp-bound-tail\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('HCEQFjHyLIw', extras=['title=\"Exponential Bounds on Tails\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-exp-bound-tail)\n",
+    "```{dropdown} 🎥 Exponential Bounds on Tails\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/HCEQFjHyLIw\" title=\"Exponential Bounds on Tails\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_20/01_Maximum_Likelihood.ipynb b/content/Chapter_20/01_Maximum_Likelihood.ipynb
index 8013ae37..d71ded5a 100644
--- a/content/Chapter_20/01_Maximum_Likelihood.ipynb
+++ b/content/Chapter_20/01_Maximum_Likelihood.ipynb
@@ -117,49 +117,12 @@
     "You can see that the value of $p$ that maximizes the likelihood looks suspiciously like $2/3$, the observed proportion of heads in our data HHHTHT. Let's see why that is true."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/AN6y89dfNCM\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1059d5cd0>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-mle-discrete\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('AN6y89dfNCM', extras=['title=\"Maximum Likelihood Discrete Case\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-mle-discrete)\n",
+    "```{dropdown} 🎥 Maximum Likelihood Discrete Case\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/AN6y89dfNCM\" title=\"Maximum Likelihood Discrete Case\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -244,48 +207,12 @@
     "To be very careful, we should check that this calculation yields a maximum and not a minimum, but given the answer you will surely accept that it's a max. You are welcome to take the second derivative of $L$ and check that we do indeed have a maximum."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/KPVK4t58zPY\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1059fe8e0>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-mle-cont\n",
-    "YouTubeVideo('KPVK4t58zPY', extras=['title=\"Maximum Likelihood Density Case\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-mle-cont)\n",
+    "```{dropdown} 🎥 Maximum Likelihood Density Case\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/KPVK4t58zPY\" title=\"Maximum Likelihood Density Case\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_20/03_Prior_and_Posterior.ipynb b/content/Chapter_20/03_Prior_and_Posterior.ipynb
index f7b70129..b5faa70d 100644
--- a/content/Chapter_20/03_Prior_and_Posterior.ipynb
+++ b/content/Chapter_20/03_Prior_and_Posterior.ipynb
@@ -58,49 +58,12 @@
     "Note the randomization: we are picking one value $p$ of $X$, and then tossing a $p$-coin $n$ times. We are not picking the coin afresh for each toss."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/EXuoy618B-k\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10fb3ecd0>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-pri-post\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('EXuoy618B-k', extras=['title=\"Prior and Posterior Densities\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-pri-post)\n",
+    "```{dropdown} 🎥 Prior and Posterior Densities\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/EXuoy618B-k\" title=\"Prior and Posterior Densities\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_21/01_Updating_and_Prediction.ipynb b/content/Chapter_21/01_Updating_and_Prediction.ipynb
index 8a658e7a..f45d9b8d 100644
--- a/content/Chapter_21/01_Updating_and_Prediction.ipynb
+++ b/content/Chapter_21/01_Updating_and_Prediction.ipynb
@@ -139,49 +139,12 @@
     "Let $S_n = I_1 + I_2 + \\cdots + I_n$ be the number of heads in the first $n$ tosses. Then the conditional distribution of $S_n$ given $X = p$ is binomial $(n, p)$. It gives you the likelihood of the observed number of heads given a value of $p$."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/NBySphqHwvw\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10a130d00>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-conj-pri\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('NBySphqHwvw', extras=['title=\"Conjugate Priors\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-conj-pri)\n",
+    "```{dropdown} 🎥 Conjugate Priors\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/NBySphqHwvw\" title=\"Conjugate Priors\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -227,48 +190,12 @@
     "$$"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/h7FmqsTfSKY\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10a1597f0>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-pred-beta\n",
-    "YouTubeVideo('h7FmqsTfSKY', extras=['title=\"Prediction\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-pred-beta)\n",
+    "```{dropdown} 🎥 Prediction\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/h7FmqsTfSKY\" title=\"Prediction\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_21/02_Beta_Binomial_Distribution.ipynb b/content/Chapter_21/02_Beta_Binomial_Distribution.ipynb
index 33fec2f1..becfe54a 100644
--- a/content/Chapter_21/02_Beta_Binomial_Distribution.ipynb
+++ b/content/Chapter_21/02_Beta_Binomial_Distribution.ipynb
@@ -74,49 +74,12 @@
     "This discrete distribution is called the *beta-binomial* distribution with parameters $r$, $s$, and $n$. It is the distribution of the number of heads in $n$ tosses of a coin that lands heads with a probability picked according to the beta $(r, s)$ distribution."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/_cc2xuBG6tY\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x108dbed90>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-nh-unif\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('_cc2xuBG6tY', extras=['title=\"Number of Heads (Uniform Prior)\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-nh-unif)\n",
+    "```{dropdown} 🎥 Number of Heads (Uniform Prior)\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/_cc2xuBG6tY\" title=\"Number of Heads (Uniform Prior)\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_22/01_Conditional_Expectation_Projection.ipynb b/content/Chapter_22/01_Conditional_Expectation_Projection.ipynb
index 2cf10848..4c387c87 100644
--- a/content/Chapter_22/01_Conditional_Expectation_Projection.ipynb
+++ b/content/Chapter_22/01_Conditional_Expectation_Projection.ipynb
@@ -223,49 +223,12 @@
     "$$"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/4-kX0R2ckMM\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10455fa90>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-proj\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('4-kX0R2ckMM', extras=['title=\"Projection\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-proj)\n",
+    "```{dropdown} 🎥 Projection\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/4-kX0R2ckMM\" title=\"Projection\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_22/02_Least_Squares_Predictor.ipynb b/content/Chapter_22/02_Least_Squares_Predictor.ipynb
index 39a7813c..1b1062a6 100644
--- a/content/Chapter_22/02_Least_Squares_Predictor.ipynb
+++ b/content/Chapter_22/02_Least_Squares_Predictor.ipynb
@@ -25,49 +25,12 @@
     "As the function that picks off the \"centers of vertical strips,\" the conditional expectation $b(X) = E(Y \\mid X)$ is a natural estimate or predictor of $Y$ given the value of $X$. We will now see how good $b(X)$ is if we use mean squared error as our criterion."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/G_3rcPnInc0\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1032dbd60>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-ls\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('G_3rcPnInc0', extras=['title=\"Least Squares\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-ls)\n",
+    "```{dropdown} 🎥 Least Squares\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/G_3rcPnInc0\" title=\"Least Squares\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -112,48 +75,12 @@
     "In terms of the scatter diagram of observed values of $X$ and $Y$, the result is saying that the best predictor of $Y$ given $X$, by the criterion of smallest mean squared error, is the average of the vertical strip at the given value of $X$."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/J-pCOpndoBI\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x103306700>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-cond-var-def\n",
-    "YouTubeVideo('J-pCOpndoBI', extras=['title=\"Conditional Variance  Definition\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-cond-var-def)\n",
+    "```{dropdown} 🎥 Conditional Variance  Definition\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/J-pCOpndoBI\" title=\"Conditional Variance  Definition\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_22/03_Variance_by_Conditioning.ipynb b/content/Chapter_22/03_Variance_by_Conditioning.ipynb
index 6d220691..8a99efc6 100644
--- a/content/Chapter_22/03_Variance_by_Conditioning.ipynb
+++ b/content/Chapter_22/03_Variance_by_Conditioning.ipynb
@@ -80,49 +80,12 @@
     "    plt.ylim(-3, 6);"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/H1aOoaIWLQQ\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x105d55ca0>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-var-cond-obs\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('H1aOoaIWLQQ', extras=['title=\"Variance by Conditioning  An Observation\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-var-cond-obs)\n",
+    "```{dropdown} 🎥 Variance by Conditioning  An Observation\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/H1aOoaIWLQQ\" title=\"Variance by Conditioning  An Observation\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -187,48 +150,12 @@
     "plt.ylim(-3, 6);"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/EWxRQv5k-uE\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x105d7e7f0>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-var-cond-form\n",
-    "YouTubeVideo('EWxRQv5k-uE', extras=['title=\"Variance by Conditioning  Formula\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-var-cond-form)\n",
+    "```{dropdown} 🎥 Variance by Conditioning  Formula\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/EWxRQv5k-uE\" title=\"Variance by Conditioning  Formula\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_22/04_Examples.ipynb b/content/Chapter_22/04_Examples.ipynb
index 568aedcd..c287d3b3 100644
--- a/content/Chapter_22/04_Examples.ipynb
+++ b/content/Chapter_22/04_Examples.ipynb
@@ -25,49 +25,12 @@
     "This section is a workout in finding expectation and variance by conditioning. As before, if you are trying to find a probability, expectation, or variance, and you think, \"If only I knew the value of this other random variable, I'd have the answer,\" then that's a sign that you should consider conditioning on that other random variable."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/-uYGQrQCXzM\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10e7c4cd0>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-mixture\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('-uYGQrQCXzM', extras=['title=\"A Mixture\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-mixture)\n",
+    "```{dropdown} 🎥 A Mixture\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/-uYGQrQCXzM\" title=\"A Mixture\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_23/01_Random_Vectors.ipynb b/content/Chapter_23/01_Random_Vectors.ipynb
index cd4f4fc7..609ca90e 100644
--- a/content/Chapter_23/01_Random_Vectors.ipynb
+++ b/content/Chapter_23/01_Random_Vectors.ipynb
@@ -142,49 +142,12 @@
     "```"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/yE1FWjup0WE\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10c646ca0>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-lt-vec\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('yE1FWjup0WE', extras=['title=\"Linear Transformation\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-lt-vec)\n",
+    "```{dropdown} 🎥 Linear Transformation\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/yE1FWjup0WE\" title=\"Linear Transformation\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -251,48 +214,12 @@
     "$$\n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/XvQa07eQDj0\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10c670460>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-cov-mat-lt\n",
-    "YouTubeVideo('XvQa07eQDj0', extras=['title=\"Covariance Matrix of Linear Transformation\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-cov-mat-lt)\n",
+    "```{dropdown} 🎥 Covariance Matrix of Linear Transformation\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/XvQa07eQDj0\" title=\"Covariance Matrix of Linear Transformation\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -320,48 +247,12 @@
     "$$"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/75RtwtxoRLw\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10c646970>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-const-cov-mat\n",
-    "YouTubeVideo('75RtwtxoRLw', extras=['title=\"Constraint on Covariance Matrix\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-const-cov-mat)\n",
+    "```{dropdown} 🎥 Constraint on Covariance Matrix\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/75RtwtxoRLw\" title=\"Constraint on Covariance Matrix\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_23/02_Multivariate_Normal_Vectors.ipynb b/content/Chapter_23/02_Multivariate_Normal_Vectors.ipynb
index 73fce2dc..eb420128 100644
--- a/content/Chapter_23/02_Multivariate_Normal_Vectors.ipynb
+++ b/content/Chapter_23/02_Multivariate_Normal_Vectors.ipynb
@@ -128,51 +128,12 @@
     "What we gain from $\\mathbf{X}$ being multivariate normal are the *shapes* of the distributions of linear transformations: they are normal. This allows us to find probabilities using the normal curve."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": "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",
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/j6xxeJ3r4iU\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1124ad010>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-lin-combo\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('j6xxeJ3r4iU', extras=['title=\"Linear Combinations\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-lin-combo)\n",
+    "```{dropdown} 🎥 Linear Combinations\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/j6xxeJ3r4iU\" title=\"Linear Combinations\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_23/03_Multivariate_Normal_Density.ipynb b/content/Chapter_23/03_Multivariate_Normal_Density.ipynb
index e8629922..ba834ac9 100644
--- a/content/Chapter_23/03_Multivariate_Normal_Density.ipynb
+++ b/content/Chapter_23/03_Multivariate_Normal_Density.ipynb
@@ -57,51 +57,12 @@
     "Thus we are going to restrict the definition of multivariate normal vectors to **invertible** linear transformations of i.i.d. standard normal vectors and **positive definite** covariance matrices."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": "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",
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/lUd7UVydEoI\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x245dbca1088>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-mvn\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('lUd7UVydEoI', extras=['title=\"Multivariate Normal\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-mvn)\n",
+    "```{dropdown} 🎥 Multivariate Normal\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/lUd7UVydEoI\" title=\"Multivariate Normal\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -129,48 +90,12 @@
     "When $n=2$ the multivariate normal distribution is called *bivariate* normal."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/ao4fqXghbmI\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10c5809d0>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-mvn-param\n",
-    "YouTubeVideo('ao4fqXghbmI', extras=['title=\"Multivariate Normal Parameters\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-mvn-param)\n",
+    "```{dropdown} 🎥 Multivariate Normal Parameters\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/ao4fqXghbmI\" title=\"Multivariate Normal Parameters\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -181,48 +106,12 @@
     "In lab you went through a detailed development of the multivariate normal joint density function, starting with $\\mathbf{Z}$ consisting of two i.i.d. standard normal components and then taking linear combinations. It turns out that all non-degenerate multivariate normal random vectors can be generated in this way. In fact, there are three useful equivalent definitions of a random vector $\\mathbf{X}$ with the multivariate normal density."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/owX4JKA-2F8\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1075bb880>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-mvn-def\n",
-    "YouTubeVideo('owX4JKA-2F8', extras=['title=\"Multivariate Normal Definition\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-mvn-def)\n",
+    "```{dropdown} 🎥 Multivariate Normal Definition\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/owX4JKA-2F8\" title=\"Multivariate Normal Definition\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -241,93 +130,21 @@
     "**The key to understanding the multivariate normal is Definition 1: every multivariate normal vector that has a density is an invertible linear transformation of i.i.d. standard normals.** Let's see what Definition 1 implies for the density."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/tIvp_0HPeIo\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10c5804f0>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-mvn-dense\n",
-    "YouTubeVideo('tIvp_0HPeIo', extras=['title=\"Multivariate Normal Density\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-mvn-dense)\n",
+    "```{dropdown} 🎥 Multivariate Normal Density\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/tIvp_0HPeIo\" title=\"Multivariate Normal Density\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/rITtghijhb4\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10cea4100>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-quad-form\n",
-    "YouTubeVideo('rITtghijhb4', extras=['title=\"Quadratic Form\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-quad-form)\n",
+    "```{dropdown} 🎥 Quadratic Form\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/rITtghijhb4\" title=\"Quadratic Form\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -434,48 +251,12 @@
     "So the quadratic form in the density of $\\mathbf{X}$ becomes $\\frac{1}{2} (\\mathbf{x} - \\mathbf{\\mu_X})^T \\boldsymbol{\\Sigma}_\\mathbf{X}^{-1} (\\mathbf{x} - \\mathbf{\\mu_X})$."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/g_S0UeHYc2k\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10cea41f0>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-mvn-vol\n",
-    "YouTubeVideo('g_S0UeHYc2k', extras=['title=\"Volume\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-mvn-vol)\n",
+    "```{dropdown} 🎥 Volume\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/g_S0UeHYc2k\" title=\"Volume\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_23/04_Independence.ipynb b/content/Chapter_23/04_Independence.ipynb
index 3359f9f8..363e40a0 100644
--- a/content/Chapter_23/04_Independence.ipynb
+++ b/content/Chapter_23/04_Independence.ipynb
@@ -32,51 +32,12 @@
     "That is, **multivariate normal random variables are independent if and only if they are pairwise uncorrelated.**"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": "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",
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/sAaWBGxZYzc\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x25f8a94fa88>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-mvn-indep\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('sAaWBGxZYzc', extras=['title=\"Independence\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-mvn-indep)\n",
+    "```{dropdown} 🎥 Independence\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/sAaWBGxZYzc\" title=\"Independence\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_24/01_Linear_Least_Squares.ipynb b/content/Chapter_24/01_Linear_Least_Squares.ipynb
index 81abcbed..d8874133 100644
--- a/content/Chapter_24/01_Linear_Least_Squares.ipynb
+++ b/content/Chapter_24/01_Linear_Least_Squares.ipynb
@@ -27,49 +27,12 @@
     "For jointly distributed random variables $X$ and $Y$, you know that $E(Y \\mid X)$ is the least squares predictor of $Y$ based on functions of $X$. We will now *restrict the allowed functions to linear functions* and see if we can find the best among those. In later sections we will see the connection between this best linear predictor, the best among all predictors, and the bivariate normal distribution."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/p-Dmvjh7MP4\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x103207cd0>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-simple-reg\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('p-Dmvjh7MP4', extras=['title=\"Simple Regression\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-simple-reg)\n",
+    "```{dropdown} 🎥 Simple Regression\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/p-Dmvjh7MP4\" title=\"Simple Regression\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_24/02_Bivariate_Normal_Distribution.ipynb b/content/Chapter_24/02_Bivariate_Normal_Distribution.ipynb
index 56b55351..571a0b95 100644
--- a/content/Chapter_24/02_Bivariate_Normal_Distribution.ipynb
+++ b/content/Chapter_24/02_Bivariate_Normal_Distribution.ipynb
@@ -160,94 +160,21 @@
     "Later in this section, we will visualize this idea in the case where the joint distribution of $X$ and $Y$ is bivariate normal."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/d-ucYswhZ_Q\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x107350640>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-constr-bvn\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('d-ucYswhZ_Q', extras=['title=\"Construction: Bivariate Normal\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-constr-bvn)\n",
+    "```{dropdown} 🎥 Construction: Bivariate Normal\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/d-ucYswhZ_Q\" title=\"Construction: Bivariate Normal\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/GQ9nsyr3umI\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x107326940>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-constr-sbvn\n",
-    "YouTubeVideo('GQ9nsyr3umI', extras=['title=\"Construction Standard Bivariate Normal\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-constr-sbvn)\n",
+    "```{dropdown} 🎥 Construction Standard Bivariate Normal\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/GQ9nsyr3umI\" title=\"Construction Standard Bivariate Normal\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_24/03_Regression_and_Bivariate_Normal.ipynb b/content/Chapter_24/03_Regression_and_Bivariate_Normal.ipynb
index 7665ef9e..41e569d1 100644
--- a/content/Chapter_24/03_Regression_and_Bivariate_Normal.ipynb
+++ b/content/Chapter_24/03_Regression_and_Bivariate_Normal.ipynb
@@ -124,49 +124,12 @@
     "bivariate_normal_regression(0.6, 1000)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/akPEba4L-wo\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10985ccd0>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-bvn-pred\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('akPEba4L-wo', extras=['title=\"Prediction\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-bvn-pred)\n",
+    "```{dropdown} 🎥 Prediction\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/akPEba4L-wo\" title=\"Prediction\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_24/04_Regression_Equation.ipynb b/content/Chapter_24/04_Regression_Equation.ipynb
index 74fa3e00..5678de33 100644
--- a/content/Chapter_24/04_Regression_Equation.ipynb
+++ b/content/Chapter_24/04_Regression_Equation.ipynb
@@ -109,49 +109,12 @@
     "1 - stats.norm.cdf(2, 0.5*2, np.sqrt(1-0.5**2))"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/hlAaKwSAXp8\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x104669cd0>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-reg-eqn\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('hlAaKwSAXp8', extras=['title=\"Regression Equation\"'])\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-reg-eqn)\n",
+    "```{dropdown} 🎥 Regression Equation\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/hlAaKwSAXp8\" title=\"Regression Equation\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -192,48 +155,12 @@
     "The conditional distribution of $Y$ given $X$ is normal with the mean and variance calculated above."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/SCY7fbSHnX8\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1046937f0>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-alt-form1\n",
-    "YouTubeVideo('SCY7fbSHnX8', extras=['title=\"Alternative Form I\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-alt-form1)\n",
+    "```{dropdown} 🎥 Alternative Form I\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/SCY7fbSHnX8\" title=\"Alternative Form I\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -258,48 +185,12 @@
     "This form shows that the regression line passes through the point $(\\mu_X, \\mu_Y)$ and that $E(\\hat{Y}) = \\mu_Y$. The predicted values and the actual values are the same *on average*."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/2kll2o4BZjI\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x104669b20>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-alt-form2\n",
-    "YouTubeVideo('2kll2o4BZjI', extras=['title=\"Alternative Form II\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown}  🎥 See More\n",
-    "![](#vid-alt-form2)\n",
+    "```{dropdown} 🎥 Alternative Form II\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/2kll2o4BZjI\" title=\"Alternative Form II\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
diff --git a/content/Chapter_25/04_Multiple_Regression.ipynb b/content/Chapter_25/04_Multiple_Regression.ipynb
index 0693370e..6c7cc0d5 100644
--- a/content/Chapter_25/04_Multiple_Regression.ipynb
+++ b/content/Chapter_25/04_Multiple_Regression.ipynb
@@ -134,49 +134,12 @@
     "Typically you will also have to estimate the unknown error variance $\\sigma^2$. But we will not cover that in this class except in the case $p = 1$."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/VLIDNbHL8u8\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10b61ad00>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-est-mot\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo('VLIDNbHL8u8', extras=['title=\"Estimate Motivation\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-est-mot)\n",
+    "```{dropdown} 🎥 Estimate Motivation\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/VLIDNbHL8u8\" title=\"Estimate Motivation\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -276,48 +239,12 @@
     "$$"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/qQG-yyUPgGE\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10b6457c0>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-sig-noise\n",
-    "YouTubeVideo('qQG-yyUPgGE', extras=['title=\"Signal and Noise\"'])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-sig-noise)\n",
+    "```{dropdown} 🎥 Signal and Noise\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/qQG-yyUPgGE\" title=\"Signal and Noise\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },