Perceptron is a single layer neural networkand a multi-layer perceptron is called Neural Networks.

Perceptron is a linear classifier (binary). Also, it is used in supervised learning. It helps to classify the given input data. But how the heck it works ?

A normal neural network looks like this as we all know

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As you can see it has multiple layers.

The perceptron consists of 4 parts.

- Input values or One input layer
- Weights and Bias
- Net sum
- Activation Function

FYI: The Neural Networks work the same way as the perceptron. So, if you want to know how neural network works, learn how perceptron works.

## But how does it work?

The perceptron works on these simple steps

a. All the inputs ** x** are multiplied with their weights

**. Letβs call it**

*w*

*k.*b. ** Add** all the multiplied values and call them

*Weighted Sum.*c. ** Apply** that weighted sum to the correct

**.**

*Activation Function*For Example: Unit Step Activation Function.

## Why do we need Weights and Bias?

**Weights** shows the strength of the particular node.

** A bias** value allows you to shift the activation function curve up or down.

## Why do we need Activation Function?

In short, **the activation functions are used to map the input between the required values like (0, 1) or (-1, 1)**.

Where we use Perceptron?

Perceptron is used to classify data into two parts,therefore is known as Linear Binary classifier.