ACTIVATION FUNCTION

Activation function decides, whether a neuron should be activated or not by calculating weighted sum and further adding bias with it. The purpose of the activation function is to introduce non-linearity into the output of a neuron.

In a neural network, we would update the weights and biases of the neurons on the basis of the error at the output. This process is known as back-propagation.

A neural network without an activation function is essentially just a linear regression model.

 https://www.geeksforgeeks.org/activation-functions-neural-networks/

https://www.geeksforgeeks.org/activation-functions/?ref=rp

https://towardsdatascience.com/everything-you-need-to-know-about-activation-functions-in-deep-learning-models-84ba9f82c253#:~:text=Simply%20put%2C%20an%20activation%20function,fired%20to%20the%20next%20neuron.