Deep Learning

A neural network is a network of neurons or, in a contemporary context, an artificial neural network made up of artificial neurons or nodes. An artificial neural network is influenced by a biological neural network. As a biological neural network is made up of true biological neurons, in the same manner, an artificial neural network is made from artificial neurons called “Perceptrons“. An artificial neural network is developed for solving artificial intelligence (AI) problems. Artificial neuron links are termed as weights. All inputs are weight-modified and summed up. This activity is called a linear combination. Finally, the output is controlled by an activation function applied over that linear combination "

In this article, we will understand the different types of activation functions and the advantage and disadvantages of those activation function"

The advantages and disadvantage of relu activation function are as explained below

In this article, we will see what is the formula to calculate the derivative of the ReLU activation function. The python code to calculate the derivative of the ReLU function is also included.

In this article, we will look at the python function to calculate the derivative of the sigmoid activation function. Sigmoid Activation Function is one of the widely used activation functions in deep learning. As its name suggests the curve of the sigmoid function is S-shaped.