gradient-descent

Go from a raw neural network to one that actually generalizes. Covers loss functions (MSE, cross-entropy), gradient descent variants (SGD, momentum, Adam), learning-rate effects, overfitting vs underfitting, and the regularization toolkit (L2/dropout/early stopping/batch norm).

Build intuition for how artificial neurons stack into layers, why nonlinear activations are non-negotiable, and how the chain rule turns a forward pass into exact gradients — illustrated with a tiny numpy forward+backward walk-through.