Neural Activation Function Explained for Advanced Users

Neural activation functions transform the sum of the weighted inputs of a neuron into an output value.

They introduce nonlinearities into the network, which is essential for the network to approximate complex functions.

Typical activation functions are the sigmoid function, the hyperbolic tangent function (tanh), and the rectified linear unit (ReLU).

Each function has its advantages and disadvantages regarding differentiability, value range, and computational effort.

The choice of activation function strongly influences the learning behavior and performance of the neural network.