# Perceptron visualization

Perceptrons are supervised learning models used to classify data into binary classes. They are one of the simplest models around, and thus serve as a good introduction to machine learning.

This page contains a running visualization of the Perceptron Learning Algorithm (PLA). First, a target function is generated randomly, and then, a set of observations is uniformly generated to populate the dataset. The learning algorithm is executed according to the lines below.

Number of points:
Generate linearly separable data

### Perceptron Learning Algorithm

0. Initialize $$\mathbf{w} \leftarrow \mathbf{0}$$
1. While there are misclassified points:
1.1. Pick a misclassified point $$\mathbf{x}_n$$
1.2. Update weights: $$\mathbf{w} \leftarrow \mathbf{w} + y_n \mathbf{x}_n$$
Iterations: 0
Misclassifications: 0