Manual Backprop
Trust the autograd, but verify it by hand.
Key Insight
Before relying entirely on autograd, it is crucial to compute the gradients manually for a simple network. By applying the chain rule step-by-step, you see exactly how the error signal flows backwards from the loss to the weights during the backward pass.
Why This Matters
Writing manual backpropagation builds a strong intuitive foundation. When you understand the math behind the gradients, you can spot and fix numerical issues, write more efficient custom operations, and truly grasp how deep learning models learn.