Activation Function

In the context of artificial neural networks, an activation function is a mathematical function that is applied to the output of each neuron in a neural network. The purpose of the activation function is to introduce non-linearity into the output of the neuron, allowing the neural network to model complex relationships between inputs and outputs. Common activation functions include the sigmoid function, the hyperbolic tangent function, and the rectified linear unit (ReLU) function. The choice of activation function can have a significant impact on the performance of a neural network, and different activation functions may be more appropriate for different types of problems or architectures.
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