Artificial Neural Network

  Artificial Neural Networks (ANN) are inspired by the human brain. ANN is made up of three layers: an input layer, a hidden layer or layers, and an output layer. These networks learn from training data and gradually improve their accuracy. Once they are trained, these networks become powerful tools to recognize patterns.
Some important terminology in ANN 

• Weights

• Bias

• Learning Rate

• Threshold

How does neural network work?
 
Once input is fed through the input layer, weights are assigned, and these weights help to understand the importance of each variable. The higher the weight, the greater the importance.
After weights are assigned, weights are multiplied with individual variables and summation is done if summation crosses a given threshold and the basis of activation function output is determined.
 
The input layer's output is routed through the hidden layer(s) and then to the output layer in a process known as "feed forward neural network."
A loss is calculated based on output and actual value. The objective is to minimize the loss value.