Activation Functions
Activation functions are mathematical functions that are used in artificial neural networks to introduce non-linearity to the output of a neuron. They are applied to the weighted sum of inputs and biases of a neuron to produce its output. The purpose of activation functions is to introduce non-linearity to the output of a neuron, which allows neural networks to model complex relationships between inputs and outputs. Common activation functions include sigmoid, tanh, ReLU, and softmax.
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