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Introduction to Perceptron

Explore the perceptron model, a foundational binary linear classifier used in supervised learning. Understand how it separates classes using weighted inputs and bias through a boundary line or plane. Learn how the step activation function is applied in prediction and how the perceptron operates as a single-layer feedforward network.

What is a perceptron?

Perceptron is a binary linear classifier used in supervised learning to classify the given input data.

Visualize a perceptron

In the case of two input data points, the perceptron model makes a line that separates the two classes.

Boundary

The boundary line that separates the two classes are:

w1x1+w2x2+b=0w_1x_1 + w_2x_2 + b = 0

Here:

  • x1x_1 and x2x_2 are the inputs
  • w1w_1 and w2w_2 are the weights
  • bb is the bias

Prediction

The predicted value on applying the step function is given by:

y^\hat{y}={\Bigg\{ 1ifβ€…β€Šw1x1+w2x2+b>=00otherwise\begin{matrix} 1 & {if\; w_1x_1 +w_2x_2 + b >= 0}\\ \\ 0 & otherwise \end{matrix} ...