Forward pass: compute activations layer by layer, get a loss.

Forward pass: compute activations layer by layer, get a loss. Backward pass: use chain rule to compute gradient of loss w.r.t. each weight. For weight W in layer l: ∂L/∂W = ∂L/∂output × ∂output/∂W. Gradients flow backward through activation derivatives (ReLU → 1 if x>0 else 0, sigmoid → σ(1-σ)). Weights updated: W = W - lr × ∂L/∂W.