PyTorch 101: Deep Learning Workflow & Classification

A comprehensive technical learning repository documenting progress through the PyTorch 101 Crash Course (2026 Edition) by Daniel Bourke. This project showcases the evolution of building deep learning models, with a focus on mastering the core PyTorch workflow: data preparation, model building, training, and evaluation.

Table of Contents

• Overview
• Key Learning Milestones
• Project Structure
• Tech Stack
• Course Reference

Overview

This repository is a hands-on learning resource that implements PyTorch fundamentals through practical examples and detailed notebook documentation. Each notebook builds upon core concepts to create functional deep learning models.

Key Learning Milestones

🔹 PyTorch Workflow Fundamentals

Implementing the end-to-end process including tensor creation, linear model construction, loss functions, and optimizer implementation.

🔹 Neural Network Classification

Designing and training models to handle non-linear classification problems (e.g., make_circles dataset) with activation functions.

🔹 Performance Evaluation

Visualizing training/testing splits and utilizing Inference Mode for efficient model predictions without gradient computation.

🔹 Model Persistence

Mastering the state_dict paradigm to save and load trained PyTorch models for reproducibility and deployment.

Project Structure

File	Description
PyTorch_101_Crash_Course_Workflow.ipynb	Foundation of the PyTorch workflow, linear regression fundamentals, and model lifecycle management
Neural_Network_Classification_with_PyTorch.ipynb	Deep dive into classification problems, activation functions, and strategies for handling non-linear data

Tech Stack

Category	Tools
Framework	PyTorch (Tensors, nn.Module, torch.optim)
Data Processing	NumPy, Pandas, Scikit-Learn
Visualization	Matplotlib

Course Reference

📺 Source: PyTorch 101 Crash Course For Beginners in 2026 by Daniel Bourke