In a neural network, an activation function normalizes the input and produces an output which is then passed forward into the subsequent layer. Activation functions add non-linearity to the output which enables neural networks to solve non-linear problems. In other words, a neural network without an activation function is essentially just a linear regression model.
Common activation functions include Linear, Sigmoid, Tanh, and ReLU but there are many others.
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