Neural Networks

Activation Functions

Functions that introduce non-linearity into neural-network computations.

An activation function determines how a neuron responds after its weighted inputs and bias are combined. Without non-linear activation functions, a deep network would behave like a single linear transformation.

Common choices include ReLU, sigmoid, tanh, GELU and softmax. Different functions affect gradient flow, training stability and the type of output produced by the network.

Also in Neural Networks

Perceptron Backpropagation Feed Forward Hopfield Network Boltzmann Machine Self Organising Maps

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