Perceptron

updated

What is a Perceptron?

A Perceptron is an algorithm used for supervised learning of binary classifiers. It is a single layer neural network and a multi-layer perceptron is called Neural Networks.

• A short description

• Neural Representation of AND, OR, NOT, XOR and XNOR Logic Gates

How it works

Binary classifier for Predict y = 1 if Wx+b > 0 , otherwise y=0

The perceptron consists of 4 parts.

1. Input values or One input layer

2. Weights and Bias

3. Net sum

4. Activation Function

Weights shows the strength of the particular node.

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

Activation Function scales output (0,1) or (-1,1)

Multi-Layer Perceptron

Multi-Layer Perceptron

Since we cannot express XOR with a single Perceptron, we can construct a network of Perceptron or Multi-Layer Perceptron

Using the previous AND, NAND, OR gates with perceptrons, we can build XOR

Activation Function