deshwalmahesh/ML-Models-from-Scratch

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

Implementation of popular ML algorithms from scratch

Minimal and clean examples of machine learning algorithms implementations

Sutskever 30 implementations inspired by https://papercode.vercel.app/ | For Agents, use https://github.com/pageman/Sutskever-Agent | Polyglot / Multi-Backed version at https://github.com/pageman/sutskever-30-beyond-numpy

Build your own X - Master machine learning by building everything from scratch. It aims to cover everything from linear regression to deep learning to large language models (LLMs).

Because `model.fit()` isn't an explanation

🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

A deep learning library built from scratch with complex neural networks examples built on top for learning purposes.