Overview / Usage

This project explores the visualization of how a perceptron learns.

Methodology / Approach

For simplicity, we consider the perceptron to learn the function y=x. We give a 2 dimensional input (x and y) coordinates, and classify each point as being below the line or above the line (binary classification). We update the weights of the perceptron whenever misclassification occurs. Over several examples, the perceptron learns the y=x mapping.

Technologies Used

Python (with TKinter graphics)

Repository

https://github.com/nmakes/neuralNetworks