Why Activation Functions Matter in Neural Networks

A neural network without activation functions is not really deep. You can stack many layers, but without nonlinearity, the model still behaves like one big linear transformation. That is why activation functions matter. They are the reason neural networks can learn curves, boundaries, patterns, and complex relationships. Core Idea An activation function transforms the output of a neuron. More importantly, it adds nonlinearity. Without it, a network cannot represent complex patterns well. With it