Backpropagation from Scratch in NumPy
A two-layer neural network trained with hand-rolled backprop in NumPy — forward pass, loss, analytic gradients via the chain rule, and a parameter update loop — no autograd, no magic.
machine-learningneural-networksdeep-learning
Forward pass, chain-rule gradients, and SGD update — backprop written out long-hand in NumPy so nothing is hidden.