ML from scratch is a repository created to understand the working of ML models.
ML models, the way they work, seem to magically give us a result. Most of the
ML libraries available hide the details while making the use of ML models very convenient.
The only way to understand them at work is through their code.
Wouldn't it be convenient for our understanding and study to implement basic versions of these algorithms from scratch? This repository is one such attempt.
We start by creating an ML implementation from scratch of a simple perceptron to gain some understanding of a neural network. Perceptrons are a basic building block of an artificial neural network(ANN).