In the human brain, DIFFERENT neurons are present. These neurons combine and perform various tasks. The Neural Network in deep learning tries to imitate human brain neurons. The neural network learns the patterns from the data and uses the knowledge that it gains from various patterns to predict the OUTPUT for new data, without any human assistance.

A perceptron is the simplest neural network that CONTAINS a SINGLE neuron that performs 2 functions. The first function is to perform the weighted sum of all the inputs and the second is an activation function.

There are some other neural networks that are more complicated. Such networks consist of the following three layers:

• Input Layer: The neural network has the input layer to receive the input.
• Hidden Layer: There can be multiple hidden layers between the input layer and the output layer. The initially hidden layers are used for detecting the low-level patterns whereas the further layers are responsible for combining output from previous layers to find more patterns.
• Output Layer: This layer outputs the prediction.

An EXAMPLE neural network image is shown below: