Popular GitHub Repositories for Iterative Linear Quadratic

100 Days of ML Coding

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.

An AI-powered research assistant that performs iterative, deep research on any topic by combining search engines, web scraping, and large language models. The goal of this repo is to provide the simplest implementation of a deep research agent - e.g. an agent that can refine its research direction overtime and deep dive into a topic.

RWKV (pronounced RwaKuv) is an RNN with great LLM performance, which can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7 "Goose". So it's combining the best of RNN and transformer - great performance, linear time, constant space (no kv-cache), fast training, infinite ctx_len, and free sentence embedding.

MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville

WebGL-accelerated ML // linear algebra // automatic differentiation for JavaScript.

CUDA Templates for Linear Algebra Subroutines

An Easy-to-use, Scalable and High-performance RLHF Framework (70B+ PPO Full Tuning & Iterative DPO & LoRA & RingAttention & RFT)

Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

Code for Tensorflow Machine Learning Cookbook

Statistical Machine Intelligence & Learning Engine

Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG

A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2025 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art techniques!

Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.

SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformer

Plain python implementations of basic machine learning algorithms

Unofficial implementation of Image Super-Resolution via Iterative Refinement by Pytorch

Spreadsheet with AI, Code, Connections

High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.