Parameter Update Rule

After computing all gradients:

\[W^{[l]} := W^{[l]} - \alpha \cdot dW^{[l]}$$ $$b^{[l]} := b^{[l]} - \alpha \cdot db^{[l]}\]

Where \(\alpha\) is the learning rate.

See Forward and Backward Propagation for the gradient derivation.