Train tiny network: forward pass on (x=2) gives 0.7 vs target 1.0, loss 0.09. Backprop computes dLoss/dw via chain rule through layers. Update w -= 0.1 * gradient. Repeat thousands of times; loss falls, predictions improve. This is how all deep learning trains.