Perceptron

Perceptron:- In general perceptron is the fundamental unit for any neural network model which acts just like a neuron in our brains

Parameters for Perceptron are Inputs (x1,x2) and Weights (w1,w2) and Bias (b1,b2) and activation function

Activation Function:

The Activation functions are functions which are used to limit the outputs based on the target classes. There are two kinds of Activation function.

1. Scalar Activation Function(used for supervised binary classifications)

2. Vector Activation Function (used for multiple target classes)

What happens in Perceptron :

Step 1:

At first Perceptron accepts the inputs and multiplied by some weights and biases in the form of Weighted sum. we represent this weighted sum as Z=summation(x1.w1+b1)

Step 2:

After getting Z we pass this to a Activation Function here in our example we used Threshold Function as our activation function

Step 3:

Based on the activation function we get the respective output based on respective input

In the architecture of Neural Network , if we had hidden layers then we simply call that network model as "Multi Layer Perceptron (MLP)"

if we don't have any hidden layers in the neural network model then we can call that network model as "Simple Layer Perceptron (SLP)"