Top Machine learning repositories on GitHub
Libraries, models, and end-to-end systems for traditional and modern ML.
Ranked by stars across 10,060 repositories tagged machine-learning. Refreshed daily.
- 1tensorflow/tensorflow★ 201,282 · ⑂ 78,170
An Open Source Machine Learning Framework for Everyone
- tensorflow
- machine-learning
- python
- deep-learning
- deep-neural-networks
- neural-network
- 2f/prompts.chat★ 175,712 · ⑂ 22,424
f.k.a. Awesome ChatGPT Prompts. Share, discover, and collect prompts from the community. Free and open source — self-host for your organization with complete privacy.
- chatgpt
- ai
- artificial-intelligence
- awesome-list
- chatgpt-prompts
- claude
- 3huggingface/transformers★ 168,496 · ⑂ 35,102
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
- nlp
- natural-language-processing
- pytorch
- pytorch-transformers
- transformer
- model-hub
- 4rasbt/LLMs-from-scratch★ 107,762 · ⑂ 16,646
Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
- gpt
- large-language-models
- llm
- python
- pytorch
- ai
- Live search
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- 5pytorch/pytorch★ 105,104 · ⑂ 31,305
Tensors and Dynamic neural networks in Python with strong GPU acceleration
- neural-network
- autograd
- gpu
- numpy
- deep-learning
- tensor
- 6microsoft/ML-For-Beginners★ 91,973 · ⑂ 22,841
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
- ml
- data-science
- machine-learning
- machine-learning-algorithms
- machinelearning
- python
- 7mlabonne/llm-course★ 84,884 · ⑂ 9,879
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
- course
- llm
- machine-learning
- roadmap
- large-language-models
- 8Developer-Y/cs-video-courses★ 84,542 · ⑂ 11,615
List of Computer Science courses with video lectures.
- computer-science
- algorithms
- systems
- databases
- machine-learning
- web-development
- 9d2l-ai/d2l-zh★ 82,232 · ⑂ 12,482
《动手学深度学习》:面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。
- deep-learning
- book
- notebook
- natural-language-processing
- computer-vision
- machine-learning
- 10netdata/netdata★ 81,263 · ⑂ 6,721
The fastest path to AI-powered full stack observability, even for lean teams.
- monitoring
- docker
- kubernetes
- cncf
- prometheus
- netdata
- Live search
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- 11tesseract-ocr/tesseract★ 77,316 · ⑂ 10,901
Tesseract Open Source OCR Engine (main repository)
- tesseract
- tesseract-ocr
- ocr
- lstm
- machine-learning
- ocr-engine
- 12rohitg00/ai-engineering-from-scratch★ 76,577 · ⑂ 13,571
Learn AI Engineering! Learn it. Build it. Ship it for others.
- agents
- ai
- ai-agents
- ai-engineering
- computer-vision
- course
- 13binhnguyennus/awesome-scalability★ 75,372 · ⑂ 7,236
The Patterns of Scalable, Reliable, and Performant Large-Scale Systems
- system-design
- backend
- scalability
- interview
- architecture
- devops
- 14openbq-org/OpenBB★ 75,057 · ⑂ 7,845
Open Data Platform for analysts, quants and AI agents.
- python
- machine-learning
- finance
- stocks
- quantitative-finance
- economics
- 15microsoft/AI-For-Beginners★ 70,537 · ⑂ 13,731
12 Weeks, 24 Lessons, AI for All!
- deep-learning
- artificial-intelligence
- machine-learning
- ai
- computer-vision
- nlp
- 16scikit-learn/scikit-learn★ 67,991 · ⑂ 27,634
scikit-learn: machine learning in Python
- machine-learning
- python
- statistics
- data-science
- data-analysis
- Live search
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- 17labmlai/annotated_deep_learning_paper_implementations★ 67,667 · ⑂ 6,785
🧑🏫 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, ... 🧠
- deep-learning
- deep-learning-tutorial
- pytorch
- gan
- transformers
- reinforcement-learning
- 18keras-team/keras★ 64,413 · ⑂ 19,824
Deep Learning for humans
- deep-learning
- tensorflow
- neural-networks
- machine-learning
- data-science
- python
- 19ultralytics/ultralytics★ 63,087 · ⑂ 11,970
Ultralytics YOLO27, YOLO26, YOLO11, YOLOv8 — object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking
- ultralytics
- yolov8
- yolo
- deep-learning
- image-classification
- machine-learning
Find engineers shipping Machine learning
The list above ranks the most-starred public repositories tagged with the Machine learning topic, drawn from the public GitHub graph. Across 10,060 repositories tagged this way, the maintainers and top contributors are a tight cluster of the people actually building Machine learning.
Looking for engineers who’ve worked on Machine learning for real, not just listed it on LinkedIn? The fastest path is the contributor list of these repos. Their commits, issues, and READMEs are public proof of depth.
Refolk turns this list into a search. Ask for “maintainers of top Machine learning repos who are hiring”, “Machine learning engineers in San Francisco”, or “founders shipping Machine learning” and Refolk returns a ranked shortlist with sources.
How this list is built
Last refreshed: Tue, 03 Nov 2026 19:23:50 GMT
Need a list like this for any search?
Refolk runs natural-language searches across GitHub, LinkedIn, and the open web. Try one of these:
- Machine learning maintainers hiringRun
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- Companies shipping Machine learningRun
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Browse other topics
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Common questions
How are these repositories ranked?
By stars, with forks and recent activity as tiebreakers, read from the public GitHub API. The methodology section above has the details.
How fresh is the data?
The ranking re-renders at least daily. Last refreshed: Tue, 03 Nov 2026 19:23:50 GMT.
Can I find the maintainers and contributors behind these repos?
Yes. Stars rank the projects; I can rank the engineers - maintainers, top contributors, and the people they work with. You start with 500 free credits, no card required.
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That's the point. I read hiring signals across GitHub, LinkedIn, and the open web, so a repo list turns into a shortlist of engineers worth talking to.
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Type one sentence. I plan the search, read GitHub, public LinkedIn and Crunchbase records, and the open web as it is right now, and hand back a ranked list with the reason next to every name.
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Ranked, with the reasoning under every name. Open a profile, ask a follow-up, narrow it down.
- Staff backend engineers in NYC who shipped Rust in production
- Series A fintechs in SF under 50 people, growing headcount this year
- Maintainers of fast-growing Rust web frameworks on GitHub
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- Read at search time, so a profile updated yesterday counts today.
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