Learn the Training Loop with PyTorch

Module 1: The Elementary Training Loop

1. Introduction: What is a Training Loop?
2. The Simplest Case: Linear Regression
3. Batch vs. Stochastic Gradient Descent
4. Visualizing the Loss Landscape
5. Numerical vs. Analytical Gradients
6. Recap and Key Takeaways

Module 2: The Training Loop for Neural Networks

1. From Linear to Nonlinear: Why Neural Networks?
2. Forward and Backward Passes
3. Implementing a Simple Neural Net from Scratch
4. The Role of Activations
5. Mini-batching and Data Pipelines
6. Regularization and Overfitting
7. Recap: Comparing Our Simple Network with Linear Regression

Module 3: Advanced Training Loops and Modern Tricks

1. Optimization Algorithms Beyond SGD
2. Learning Rate Scheduling
3. Weight Initialization
4. Deeper Networks and Backprop Challenges
5. Large-Scale Training: Data Parallelism and Hardware
6. Monitoring and Debugging the Training Loop
7. Modern Regularization and Generalization Techniques
8. The Training Loop in Practice: Case Studies
9. Conclusion: What’s Next After the Training Loop?