Backpropagation: Gradients via the Chain Rule and MNIST in NumPy
Part 2 of AI Note. We follow backpropagation through the chain rule and computational graphs, starting from numeric examples, derive the gradients of a two-layer network in matrix form, and implement it from scratch in NumPy to reach 97.7% on MNIST. We also measure why minibatches are fast.
Sunhong'sSystem Blog
AI Note