An activation function, also known as a transfer function, plays a vital role in neural networks. It is used to introduce non-linearity in neural networks. As we learned before, we apply the activation function to the input, which is multiplied by weights and added to the bias, that is, 
, where z = (input * weights) + bias and 
is the activation function. If we do not apply the activation function, then a neuron simply resembles the linear regression. The aim of the activation function is to introduce a non-linear transformation to learn the complex underlying patterns in the data.
Now let's look at some of the interesting commonly used activation functions.
