*This article explains a very brief overview of the perceptron algorithm*

A perceptron was introduced by Frank Rosenblatt in 1957. He proposed the idea of a perceptron algorithm used for binary classification of data. Perceptron is a mathematical computational model for classifying only linearly separable data. Perceptron algorithm only works if the data is linearly separable.

It works by finding a hyperplane that splits the data such that all similar data points lie on either side of the hyperplane.

In theory there exists infinitely many such hyperplanes. The Perceptron algorithm might not necessarily find the optimal hyperplane. It stops as soon as all the data points are correctly separated.

A Perceptron takes the weighted sum of inputs and if the sum is more than 0 it outputs 1 and -1 if sum is less than 0.

But what happens if the point line on the hyperplane ie the weighted sum is equal to 0.

This point is still consider a misclassification. If the algorithm encounter any point that lies on the hyperplane it slightly nudge the parameters such that the point falls on either side of the hyperplane.

Let *X* and *W* be two vectors of inputs and weights respectively.