Calculus.

Calculus is essential for understanding how neural networks learn. Derivatives measure how outputs change with inputs, enabling gradient-based optimization. The chain rule is fundamental to backpropagation. Gradients indicate the direction of steepest increase, guiding parameter updates to minimize loss.