🧑🏫 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, ... 🧠
Repository Index
47,133 repositories indexed
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Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.
Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process.
Streamlit — A faster way to build and share data apps.
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
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.
Machine Learning Systems: Foundations, Scaling, Agentic AI, and Physical AI (Vols I–IV) • Harvard CS249r | https://mlsysbook.ai
JARVIS, a system to connect LLMs with ML community. Paper: https://arxiv.org/pdf/2303.17580.pdf
100-Days-Of-ML-Code中文版
☁️ Build multimodal AI applications with cloud-native stack
Open standard for machine learning interoperability
ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in — works with Claude Code, Codex, OpenClaw, or any LLM agent.
Python package built to ease deep learning on graph, on top of existing DL frameworks.
An open source implementation of CLIP.
1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
A Library for Advanced Deep Time Series Models for General Time Series Analysis.
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Low-code framework for building custom LLMs, neural networks, and other AI models
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
Minimal and clean examples of machine learning algorithms implementations
Fast and Accurate ML in 3 Lines of Code
The AI Compute Platform for frontier teams. SkyPilot turns fragmented AI compute into one AI supercomputer, so frontier AI teams build custom intelligence faster.
PyTorch implementation of YOLOv3, YOLOv3-SPP, and YOLOv3-tiny for real-time object detection with training, validation, inference, and multi-format export.
AutoML library for deep learning