Machine-Learning

Why we included this project

This repo implements machine learning with plain NumPy and reads like a set of lecture notes someone actually took the time to write. It walks through feed-forward networks, gradient descent, backpropagation, and the chain rule step by step, with working Python and the math written out beside the code. That makes it a good fit for students preparing for ML interviews and developers who want to see what sits underneath framework abstractions, since every line is visible and NumPy is the only dependency. The same approach extends into a transformer and distributed model implementations, so there is more to chew on than the usual intro tutorial. Just treat it as a study reference to work through notebook by notebook, not a polished framework for production.