Activation functions. Activation functions are mathematical…

Sigmoid function: The sigmoid function is a widely used activation function that maps the input to a value between 0 and 1. It is defined as follows: sigmoid(x) 1 / (1 exp(-x)). It is useful for binary classification tasks where the output is a probability. However, it has a tendency to saturate, meaning that for large values of x, the derivative becomes very small, which can slow down training. To use sigmoid function in a neural network, you can use the sigmoid activation function provided by most deep learning frameworks. ReLU function: The rectified linear unit (ReLU) function is another popular activation function that is widely used in deep neural networks.