Debugging

What Are We Actually Doing?
The Tensor: What Is Actually Flowing Through the Loop?
Autograd: What Did PyTorch Record, and Where Does the Gradient Stop?
The Network: What Is It Without nn.Module?
nn.Module: What Does PyTorch Think Belongs to Your Model?
DataLoader: Where Is the Training Loop Actually Waiting?
Transforms: What Does the Model Actually See?
CNN Geometry: What Shape Reaches the Next Layer?
Feature Space: What Does a Linear Model Actually See?
Attention: Which Position Is Comparing With Which?
Training: Which Link in the Learning Chain Is Broken?
Compilation: Which Assumption Stopped Holding?
Regressions: Did the Model Change, or the Measurement?
Assembly: A Language Model You Can Interrogate
Appendix A: PyTorch Diagnostic Field Guide
Advanced Agents From First Principles 13: How Do You Debug an Agent That Made the Wrong Decision? Add Trajectory Observability
Debugging Jupyter Notebooks in VS Code