ML-From-Scratch

Why we included this project

Most machine learning libraries hand you a trained model and hide the mechanics. This project does the opposite: every algorithm, from linear regression to deep learning, is written in plain NumPy with the math out in the open. You can read the actual code and trace how gradients flow and where backpropagation happens, instead of trusting a framework to sort it out. The repo ships runnable examples, like classifying digits with a CNN or evolving a network with a genetic algorithm, so you get concrete starting points to experiment with. It is not something you would deploy in production, but for anyone who wants to genuinely understand what scikit-learn or PyTorch do under the hood, it is a remarkably complete learning resource.