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.
- 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 w. Let’s call it 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.