Artificial Neural Network (ANN) is a computational model based on the structure of the Biological Neural Network(BNN). The human brain has billions of neurons that collect, process the information, and drive meaningful results out of it. The neurons use electro-chemical signals to communicate and pass the information to other neurons. Similarly, ANN consists of artificial neurons called nodes connected with other nodes forming a complex relationship between the output and the input.
There are three layers in the Artificial Neural Network:
Input Layer:  The input layer has neurons that take the input from external sources like files, data sets, images, videos, and sensors. This part of the Neural Network doesn’t perform any computation.  It only transfers the data from the outside world to the Neural Network
Hidden Layer: The hidden layer receives the data from the input layer and uses it to derive results and train several Machine Learning models. The layer can be further divided into sub-layers that extract features, make decisions, connect with other sources, and predict future actions based on the events that happened.
Output layer: After processing, the data is transferred to the output layer for delivering it to the outside environment.
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