The activation function is used to introduce nonlinearity into the neural network so that it can learn more complex function. Without the Activation function, the neural network would be only able to learn function, which is a linear combination of its input data.
Activation function translates the inputs into outputs. The activation function is responsible for deciding whether a neuron should be activated or not. It makes the decision by calculating the weighted sum and further adding bias with it. The basic purpose of the activation function is to introduce non-linearity into the output of a neuron.
