Activation function is the main computation core behind the artificial intelligence mostly for the Neural Network, and today will try to overview some of them by giving a short introduction and a clear example of the usual use case.

## Binary step function

The *Binary step function *or the “**Heaviside step function”**, Is a function represent a signal that switches on a specific value or after a specific time a threshold. The binary step function is used mostly with one single perceptron neural network and used to separate linearly between two classes. But there’s a little caveat behind the uses of binary step function on NN, based on calculus the gradient descent of the step function is always 0 which represent no rating change for updating weights.

In the next we could find a “python” implementation of the Binary step function.