F23 Q1b

Answer — step by step

Hyperparameters are chosen outside the ordinary weight-gradient updates. Learning rate controls step size; number of layers and units controls capacity; activation determines nonlinear behavior; batch size controls the data used per update and memory demand; epochs control training duration. Regularization strength, dropout rate and early-stopping patience affect generalization. Optimizer-specific choices include momentum β or Adam β₁, β₂,ε. Tune using validation data while keeping the final test set separate. Weights and biases are learned parameters; they are not hyperparameters merely because their initial values must be selected.