Developer Gains Clarity on Why Activation Function Derivatives Drive Neural Network Learning

A developer shared a learning breakthrough while implementing five activation functions — Sigmoid, Tanh, ReLU, Leaky ReLU, and Softmax — from scratch in Python. The key insight was that activation functions govern a neuron's output during the forward pass, while their derivatives control how weights are updated during backpropagation. Visualizing the derivatives revealed practical trade-offs: Sigmoid's near-zero gradients cause the vanishing gradient problem, while ReLU's simple derivative enables faster training in deep networks. Leaky ReLU addresses ReLU's weakness by assigning a small negative slope to prevent neurons from permanently stopping learning. The developer plans to next build forward propagation, backpropagation, and gradient descent from scratch without relying on frameworks like TensorFlow or PyTorch.