Machine-Learning-Collection

Why we included this project

This repo does two jobs at once: it is a structured course for people new to deep learning, and a reference you flip back to when you forget how a piece fits together. Code examples walk through each algorithm from linear regression up through GANs and transformer-style architectures, and most have YouTube walkthroughs so you can watch the reasoning before running anything. PyTorch and TensorFlow are both covered, which helps if you switch frameworks or just want to compare idioms. Later, when you need to recall how a training loop or loss function is wired, the snippets are short enough to copy and adapt, and the notebooks let you experiment interactively. It suits self-taught learners as well as engineers refreshing their fundamentals.