What the Hell is Perceptron?

The Fundamentals of Neural Networks

Perceptron is a single layer neural network and 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.

1. Input values or One input layer
2. Weights and Bias
3. Net sum
4. 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.

Fig : Perceptron

But how does it work?

The perceptron works on these simple steps

a. All the inputs x are multiplied with their weights w. Let’s call it k.

Fig: Multiplying inputs with weights for 5 inputs

b. Add all the multiplied values and call them Weighted Sum.

Fig: Adding with Summation

c. Apply that weighted sum to the correct Activation Function.

For Example: Unit Step Activation Function.

Fig: Unit Step Activation Function