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
diff --git a/content/Chapter_01/03_Collisions_in_Hashing.ipynb b/content/Chapter_01/03_Collisions_in_Hashing.ipynb
index 8f21a1bf..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\")"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 c1148d3c..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')"
-   ]
-  },
   {
    "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\")"
-   ]
-  },
   {
    "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 05c0b25c..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\")"
-   ]
-  },
   {
    "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 39aed6d0..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\")"
-   ]
-  },
   {
    "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\")"
-   ]
-  },
   {
    "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 2176acc3..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\")"
-   ]
-  },
   {
    "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\")"
-   ]
-  },
   {
    "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 3f3c8cc6..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": "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",
-      "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\")"
-   ]
-  },
   {
    "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\")"
-   ]
-  },
   {
    "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 d154a5fe..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\")"
-   ]
-  },
   {
    "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\")"
-   ]
-  },
   {
    "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\")"
-   ]
-  },
   {
    "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 2d552a26..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\")"
-   ]
-  },
   {
    "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 7be09f86..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\")"
-   ]
-  },
   {
    "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\")"
-   ]
-  },
   {
    "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 4a558e50..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\")"
-   ]
-  },
   {
    "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 1d49c994..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\")"
-   ]
-  },
   {
    "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\")"
-   ]
-  },
   {
    "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 a33537f0..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\")"
-   ]
-  },
   {
    "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 a47f1333..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\")"
-   ]
-  },
   {
    "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 8385e027..6debd3a7 100644
--- a/content/Chapter_05/02_Inclusion_Exclusion.ipynb
+++ b/content/Chapter_05/02_Inclusion_Exclusion.ipynb
@@ -29,49 +29,12 @@
     "$$"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/HdLtwQ1R4m8\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x1042e9ca0>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-ie-3e\n",
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"HdLtwQ1R4m8\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-ie-3e)\n",
+    "```{dropdown} 🎥 Inclusion-Exclusion: Three Events\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/HdLtwQ1R4m8\" title=\"Inclusion-Exclusion: Three Events\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -93,48 +56,12 @@
     "$$"
    ]
   },
-  {
-   "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\")"
-   ]
-  },
   {
    "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\")"
-   ]
-  },
   {
    "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 de5263fc..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\")"
-   ]
-  },
   {
    "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\")"
-   ]
-  },
   {
    "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 a707e621..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\")\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 5279090b..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\")"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 0c5a3a46..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\")"
-   ]
-  },
   {
    "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\")"
-   ]
-  },
   {
    "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\")"
-   ]
-  },
   {
    "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 cfb8040b..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\")"
-   ]
-  },
   {
    "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\")"
-   ]
-  },
   {
    "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 efc60cde..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 4fae6d33..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 9ebb8344..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 c3b5f39f..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 c0f74b6a..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 887e4a12..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 ab9ce015..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 61e1016d..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')"
-   ]
-  },
   {
    "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 942cbe92..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 3938e187..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 0d967949..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 2371b6e0..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 e49d2f95..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 443898ae..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 3cc42d55..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 a9ab3efb..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 1b4ade46..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 69968354..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')"
-   ]
-  },
   {
    "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 021b3969..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 fac95078..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 49689a66..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 67692c72..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 8dc34d6c..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 4b08ed49..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 3857b50f..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 aeded676..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 f6162e53..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 213fe068..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 034daff1..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 3401c6af..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')"
-   ]
-  },
   {
    "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 9d5e05e1..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')"
-   ]
-  },
   {
    "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 a6524c12..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 769fe0e4..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')"
-   ]
-  },
   {
    "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 3c70c366..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')"
-   ]
-  },
   {
    "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 8550ce93..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 91038056..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')"
-   ]
-  },
   {
    "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 a6bc2058..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 1a0879c2..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": "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",
-      "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')"
-   ]
-  },
   {
    "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 24010d87..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 49765192..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')"
-   ]
-  },
   {
    "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 dc6883dc..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')"
-   ]
-  },
   {
    "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 615c8588..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 2af138ab..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')"
-   ]
-  },
   {
    "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 16a8cb47..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')"
-   ]
-  },
   {
    "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 97b61340..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 2812f25a..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')"
-   ]
-  },
   {
    "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 cfe0a9d9..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 f13ecee3..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')"
-   ]
-  },
   {
    "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 b8a07fa1..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')"
-   ]
-  },
   {
    "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 a5d8282e..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 4a5226d4..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 4f6fccea..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')"
-   ]
-  },
   {
    "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 b8d7ccfd..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')"
-   ]
-  },
   {
    "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 @@
     "$$\n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"400\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/XvQa07eQDj0\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x10c670460>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#| label: vid-cov-mat-lt\n",
-    "YouTubeVideo('XvQa07eQDj0')"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{dropdown} 🎥 See More\n",
-    "![](#vid-cov-mat-lt)\n",
+    "```{dropdown} 🎥 Covariance Matrix of Linear Transformation\n",
+    "<iframe width=\"400\" height=\"300\" src=\"https://www.youtube.com/embed/XvQa07eQDj0\" title=\"Covariance Matrix of Linear Transformation\" frameborder=\"0\" allowfullscreen></iframe>\n",
     "```"
    ]
   },
@@ -320,48 +247,12 @@
     "$$"
    ]
   },
-  {
-   "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')"
-   ]
-  },
   {
    "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 058ccc96..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')"
-   ]
-  },
   {
    "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 7723dd74..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 f3a50f9b..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')"
-   ]
-  },
   {
    "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 8adaa795..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')"
-   ]
-  },
   {
    "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 c6ec732c..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 855027e7..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')"
-   ]
-  },
   {
    "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 37a593c3..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')\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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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 7be89616..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')"
-   ]
-  },
   {
    "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')"
-   ]
-  },
   {
    "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",
     "```"
    ]
   },