Activation functions: how a neuron decides whether to "fire" or not

Without activation functions, a neural network is just matrix multiplication. No matter how many layers you add, the result stays a linear function of the input. The activation function introduces nonlinearity — that is what gives the network the ability to learn complex patterns. But the choice of function matters: the wrong one will "kill" gradients, and the network will not learn.

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