Why Neural Networks Need Activation Functions: A Developer's Insight

A developer explains that activation functions determine neuron output, but their derivatives are crucial for learning via backpropagation. Implementing Sigmoid, Tanh, ReLU, Leaky ReLU, and Softmax from scratch reveals how derivatives guide weight updates. The vanishing gradient problem in Sigmoid and the efficiency of ReLU are highlighted through practical experiments.