22 best learning GitHub repos to use
Courses, cookbooks, and worked examples for learning to build with AI. Repos that teach rather than repos that ship.
Find GitHub repos worth using by topic, language, license and how active they are. Navid's picks come first, and each one opens its own page.
This is a list of the best learning GitHub repos.
In fact, it has 22 of them, with Navid's picks first.
So if you want learning GitHub repos worth your time, you'll love this list.
Courses, cookbooks, and worked examples for learning to build with AI. Repos that teach rather than repos that ship.
Here's what's inside:
- Project Based Learning by practical-tutorials
- Free Domain by DigitalPlatDev
- OpenAI Cookbook by OpenAI
- OpenBB by OpenBB-finance
- AI For Beginners by microsoft
- Nanogpt by karpathy
- Claude Cookbooks by anthropics
- Google Research by google-research
- Everyone Can Use English by ZuodaoTech
- Dspy by stanfordnlp
- FinceptTerminal by Fincept-Corporation
- Nautilus Trader by nautechsystems
- Nn Zero To Hero by karpathy
- Easy Vibe by datawhalechina
- Llama Cookbook by meta-llama
- Skypilot by skypilot-org
- Finrobot by AI4Finance-Foundation
- Cloudflare Docs by Cloudflare
- Higgsfield by Higgsfield
- Autotrain Advanced by huggingface
- Libero by Lifelong-Robot-Learning
- Nemotron by NVIDIA-NeMo
Each one comes with what it covers and who it's for.
What are the best learning GitHub repos?
Here's the list at a glance.
- Owner
- practical-tutorials
- Stars
- ★ 285k
- Owner
- DigitalPlatDev
- Stars
- ★ 200k
- Owner
- OpenAI
- Stars
- ★ 76k
- Owner
- OpenBB-finance
- Stars
- ★ 73k
- Owner
- microsoft
- Stars
- ★ 69k
- Owner
- karpathy
- Stars
- ★ 63k
- Owner
- anthropics
- Stars
- ★ 53k
- Owner
- google-research
- Stars
- ★ 39k
- Owner
- ZuodaoTech
- Stars
- ★ 38k
- Owner
- stanfordnlp
- Stars
- ★ 38k
- Owner
- Fincept-Corporation
- Stars
- ★ 32k
- Owner
- nautechsystems
- Stars
- ★ 29k
- Owner
- karpathy
- Stars
- ★ 25k
- Owner
- datawhalechina
- Stars
- ★ 20k
- Owner
- meta-llama
- Stars
- ★ 19k
- Owner
- skypilot-org
- Stars
- ★ 11k
- Owner
- AI4Finance-Foundation
- Stars
- ★ 8.1k
- Owner
- Cloudflare
- Stars
- ★ 5.2k
- Owner
- Higgsfield
- Stars
- ★ 4.6k
- Owner
- huggingface
- Stars
- ★ 4.6k
- Owner
- Lifelong-Robot-Learning
- Stars
- ★ 2.4k
- Owner
- NVIDIA-NeMo
- Stars
- ★ 2.1k
Top 22 learning GitHub repos
1. Project Based Learning by practical-tutorials
A list of programming tutorials in which aspiring software developers learn how to build an application from scratch. These tutorials are divided into different primary programming languages. Tutorials may involve multiple technologies and languages.
Stars: 285k
Language: Python
License: MIT
View Project Based Learning on GitHub · More about Project Based Learning
2. Free Domain by DigitalPlatDev
Free domain registration and practical DNS learning resources for everyone.
Register a domain, connect your preferred DNS provider through custom nameservers, and use the included learning guide to take a project from registration to deployment.
This repository contains the public information and learning resources for DigitalPlat FreeDomain. The application source code is maintained separately in DigitalPlatDev/Domain-OSS.
Stars: 200k
Language: Markdown
License: AGPL-3.0
View Free Domain on GitHub · More about Free Domain
3. OpenAI Cookbook by OpenAI
Example code and guides for accomplishing common tasks with the OpenAI API. To run these examples, you'll need an OpenAI account and associated API key (create a free account here). Set an environment variable called OPENAIAPIKEY with your API key. Alternatively, in most IDEs such as Visual Studio Code, you can create an.env file at the root of your repo containing OPENAIAPIKEY=, which will be picked up by the notebooks.
Most code examples are written in Python, though the concepts can be applied in any language.
For other useful tools, guides and courses, check out these related resources from around the web.
Stars: 76k
Language: Jupyter Notebook
License: MIT
View OpenAI Cookbook on GitHub · More about OpenAI Cookbook
4. OpenBB by OpenBB-finance
Open Data Platform by OpenBB (ODP) is the open-source toolset that helps data engineers integrate proprietary, licensed, and public data sources into downstream applications like AI copilots and research dashboards.
ODP operates as the "connect once, consume everywhere" infrastructure layer that consolidates and exposes data to multiple surfaces at once: Python environments for quants, OpenBB Workspace and Excel for analysts, MCP servers for AI agents, and REST APIs for other applications.
While the Open Data Platform provides the open-source data integration foundation, OpenBB Workspace offers the enterprise UI for analysts to visualize datasets and leverage AI agents. The platform's "connect once, consume everywhere" architecture enables seamless integration between the two.
Stars: 73k
Language: Python
License: Other
Install:
bash pip install "openbb[all]"
View OpenBB on GitHub · More about OpenBB
5. AI For Beginners by microsoft
Explore the world of Artificial Intelligence (AI) with our 12-week, 24-lesson curriculum! It includes practical lessons, quizzes, and labs. The curriculum is beginner-friendly and covers tools like TensorFlow and PyTorch, as well as ethics in AI
This repository includes 50+ language translations which significantly increases the download size. To clone without translations, use sparse checkout:
This gives you everything you need to complete the course with a much faster download.
Stars: 69k
Language: Jupyter Notebook
License: MIT
View AI For Beginners on GitHub · More about AI For Beginners
6. Nanogpt by karpathy
Update Nov 2025 nanoGPT has a new and improved cousin called nanochat. It is very likely you meant to use/find nanochat instead. nanoGPT (this repo) is now very old and deprecated but I will leave it up for posterity.
The simplest, fastest repository for training/finetuning medium-sized GPTs. It is a rewrite of minGPT that prioritizes teeth over education. Still under active development, but currently the file train.py reproduces GPT-2 (124M) on OpenWebText, running on a single 8XA100 40GB node in about 4 days of training. The code itself is plain and readable: train.py is a ~300-line boilerplate training loop and model.py a ~300-line GPT model definition, which can optionally load the GPT-2 weights from OpenAI. That's it.
Because the code is so simple, it is very easy to hack to your needs, train new models from scratch, or finetune pretrained checkpoints (e.g. biggest one currently available as a starting point would be the GPT-2 1.3B model from OpenAI).
Stars: 63k
Language: Python
License: MIT
Install:
bash pip install torch numpy transformers datasets tiktoken wandb tqdm
View Nanogpt on GitHub · More about Nanogpt
7. Claude Cookbooks by anthropics
The Claude Cookbooks provide code and guides designed to help developers build with Claude, offering copy-able code snippets that you can easily integrate into your own projects.
To make the most of the examples in this cookbook, you'll need a Claude API key (sign up for free here).
While the code examples are primarily written in Python, the concepts can be adapted to any programming language that supports interaction with the Claude API.
Stars: 53k
Language: Jupyter Notebook
License: MIT
View Claude Cookbooks on GitHub · More about Claude Cookbooks
8. Google Research by google-research
All datasets in this repository are released under the CC BY 4.0 International license, which can be found here: https://creativecommons.org/licenses/by/4.0/legalcode. All source files in this repository are released under the Apache 2.0 license, the text of which can be found in the LICENSE file.
Because the repo is large, we recommend you download only the subdirectory of interest: Use GitHub editor to open the project. To open the editor change the url from github.com to github.dev in the address bar. In the left navigation panel, right-click on the folder of interest and select download.
If you'd like to submit a pull request, you'll need to clone the repository; we recommend making a shallow clone (without history).
Stars: 39k
Language: Jupyter Notebook
License: Apache-2.0
Install:
bash git clone git@github.com:google-research/google-research.git --depth=1
View Google Research on GitHub · More about Google Research
9. Everyone Can Use English by ZuodaoTech
人人都能用英语
Stars: 38k
Language: TypeScript
License: GPL-3.0
View Everyone Can Use English on GitHub · More about Everyone Can Use English
10. Dspy by stanfordnlp
DSPy is the framework for programming, rather than prompting, language models. It allows you to iterate fast on building modular AI systems and offers algorithms for optimizing their prompts and weights, whether you're building simple classifiers, sophisticated RAG pipelines, or Agent loops.
DSPy stands for Declarative Self-improving Python. Instead of brittle prompts, you write compositional Python code and use DSPy to teach your LM to deliver high-quality outputs. Learn more via our official documentation site or meet the community, seek help, or start contributing via this GitHub repo and our Discord server.
If you're looking to understand the framework, please go to the DSPy Docs at dspy.ai.
Stars: 38k
Language: Python
License: MIT
Install:
bash pip install dspy
View Dspy on GitHub · More about Dspy
11. FinceptTerminal by Fincept-Corporation
Two editions. Enterprise is the private, closed-source build for funds and research desks, 41 modules, private data, live broker routing, SSO, from $99/user/month. This repo is the free AGPL-3.0 edition for learning and academic use, one release a month. Compare · Pricing
State-of-the-art financial intelligence platform with institutional-grade financial analytics, AI automation, and unlimited data connectivity.
Fincept Terminal is a native C++20 desktop terminal for financial research, Qt6 UI, embedded Python 3.11 analytics, one binary, no Electron.
Stars: 32k
Language: C++
License: Other
View FinceptTerminal on GitHub · More about FinceptTerminal
12. Nautilus Trader by nautechsystems
Platform Rust Python:-----------------:-----:-------- Linux (x8664) 1.97.1 3.12-3.14 Linux (ARM64) 1.97.1 3.12-3.14 macOS (ARM64) 1.97.1 3.12-3.14 Windows (x8664) 1.97.1 3.12-3.14 Docs: Website: Support: support@nautilustrader.io
NautilusTrader is an open-source, production-grade, Rust-native engine for multi-asset, multi-venue trading systems.
The system spans research, deterministic simulation, and live execution within a single event-driven architecture, with Python serving as the control plane for strategy logic, configuration, and orchestration.
Stars: 29k
Language: Rust
License: LGPL-3.0
Install:
bash pip install -U nautilus_trader --pre --index-url=https://packages.nautechsystems.io/simple
View Nautilus Trader on GitHub · More about Nautilus Trader
13. Nn Zero To Hero by karpathy
A course on neural networks that starts all the way at the basics. The course is a series of YouTube videos where we code and train neural networks together. The Jupyter notebooks we build in the videos are then captured here inside the lectures directory. Every lecture also has a set of exercises included in the video description. (This may grow into something more respectable).
Backpropagation and training of neural networks. Assumes basic knowledge of Python and a vague recollection of calculus from high school. YouTube video lecture Jupyter notebook files micrograd Github repo
We implement a bigram character-level language model, which we will further complexify in followup videos into a modern Transformer language model, like GPT. In this video, the focus is on (1) introducing torch.Tensor and its subtleties and use in efficiently evaluating neural networks and (2) the overall framework of language modeling that includes model training, sampling, and the evaluation of a loss (e.g. the negative log likelihood for classification). YouTube video lecture Jupyter notebook files makemore Github repo
Stars: 25k
Language: Jupyter Notebook
License: MIT
View Nn Zero To Hero on GitHub · More about Nn Zero To Hero
14. Easy Vibe by datawhalechina
Learn AI coding from zero by shipping real products. 从零开始学 AI 编程，把想法真正做成产品。
你好 · Hello · 哈囉 · こんにちは · 안녕하세요 · Hola · Bonjour · Hallo · مرحبا · Xin chào Our tutorial supports 10 languages. Let's code together! 我们的教程支持 10 种语言，欢迎世界各地的朋友一起 coding！
Have your own vibe coding story? Submit it here and inspire others! Why Easy-Vibe News Who This Is For Your Learning Paths Study Suggestions I. Beginner Entry II. Junior and Mid-Level Developers III. Advanced Developers Appendix Knowledge Base How To Learn Run Locally Other Courses Contributing & Contributors LICENSE
Stars: 20k
Language: JavaScript
View Easy Vibe on GitHub · More about Easy Vibe
15. Llama Cookbook by meta-llama
Welcome to the official repository for helping you get started with inference, fine-tuning and end-to-end use-cases of building with the Llama Model family.
This repository covers the most popular community approaches, use-cases and the latest recipes for Llama Text and Vision models. Get started with Llama API Integrate Llama API with WhatsApp 5M long context using Llama 4 Scout Analyze research papers with Llama 4 Maverick Create a character mind map from a book using Llama 4 Maverick 3P Integrations: Getting Started Recipes and End to End Use-Cases from various Llama providers End to End Use Cases: As the name suggests, spanning various domains and applications Getting Started: Reference for inferencing, fine-tuning and RAG examples src: Contains the src for the original llama-recipes library along with some FAQs for fine-tuning.
Note: We recently did a refactor of the repo, archive-main is a snapshot branch from before the refactor Q: What happened to llama-recipes? A: We recently renamed llama-recipes to llama-cookbook. Q: I have some questions for Fine-Tuning, is there a section to address these? A: Check out the Fine-Tuning FAQ here. Q: Some links are broken/folders are missing: A: We recently did a refactor of the repo, archive-main is a snapshot branch from before the refactor. Q: Where can we find details about the latest models? A: Official Llama models website.
Stars: 19k
Language: Jupyter Notebook
License: MIT
View Llama Cookbook on GitHub · More about Llama Cookbook
16. Skypilot by skypilot-org
SkyPilot is a system to run, manage, and scale AI workloads on any AI infrastructure.
SkyPilot gives AI teams a simple interface to run jobs on any infra. Infra teams get a unified control plane to manage any AI compute, with advanced scheduling, scaling, and orchestration.
fire: News:fire: [Aug 2026] RL is bottlenecked by inference: scale it independently with SkyPilot: blog [Jul 2026] Serving Kimi K3 on your own GPUs with SkyPilot: blog [Jul 2026] SkyPilot v0.13.0 released: Hugging Face storage, batch inference abstractions, lifecycle hooks, governance & robustness on API server: Release notes [Jun 2026] SkyPilot Endpoints: production-ready inference on every cluster you own: blog [Jun 2026] Announcing SkyPilot Sandboxes: run untrusted, LLM-generated code on the Kubernetes clusters you already own. Learn more, join early access [May 2026] How Multiverse doubled their GPU utilization with SkyPilot: case study [Apr 2026] Introducing GPU Compass: One dashboard to browse, compare pricing, and launch across every GPU cloud. Try it at gpus.skypilot.co. [Apr 2026] Research-Driven Agents: Agents read arxiv papers before coding, landed 5 llama.cpp kernel fusions and +15% faster flash attention in ~3 hours for ~$29: blog, HackerNews [Mar 2026] Scaling Karpathy's Autoresearch: Autoresearch runs 1 experiment at a time. We gave it 16 GPUs and let it run in parallel: blog, HackerNews [Mar 2026] How H Company Unlocked Online RL and Unified their AI Platform: case study
Stars: 11k
Language: Python
License: Apache-2.0
Install:
bash pip install -r requirements.txt
View Skypilot on GitHub · More about Skypilot
17. Finrobot by AI4Finance-Foundation
FinRobot is an AI Agent platform tailored for financial applications, surpassing FinGPT's single-model approach. It unifies multiple AI technologies, including LLMs, reinforcement learning, and quantitative analytics, to power investment research automation, algorithmic trading strategies, and risk assessment, delivering a full-stack intelligent solution for the financial industry.
Concept of AI Agent: an AI Agent is an intelligent entity that uses large language models as its brain to perceive its environment, make decisions, and execute actions. Unlike traditional artificial intelligence, AI Agents possess the ability to independently think and utilize tools to progressively achieve given objectives.
We are excited to announce the first public release of FinRobot Desktop v0.1.0, a native desktop equity research cockpit powered by a production-grade multi-agent architecture.
Stars: 8.1k
Language: Jupyter Notebook
License: Apache-2.0
Install:
bash git clone https://github.com/AI4Finance-Foundation/FinRobot.git
View Finrobot on GitHub · More about Finrobot
18. Cloudflare Docs by Cloudflare
Welcome to the open-source repository for all Cloudflare Developer Documentation.
To learn how to contribute, visit the contribution page of the Cloudflare Style Guide.
Except as otherwise noted, Cloudflare and any contributors grant you a license to the Cloudflare Developer Documentation and other content in this repository under the Creative Commons Attribution 4.0 International Public License, see the LICENSE file, and grant you a license to any code in the repository under the MIT License, see the LICENSE-CODE file.
Stars: 5.2k
Language: MDX
License: CC-BY-4.0
View Cloudflare Docs on GitHub · More about Cloudflare Docs
19. Higgsfield by Higgsfield
Higgsfield is an open-source, fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters, such as Large Language Models (LLMs).
- Allocating exclusive and non-exclusive access to compute resources (nodes) to users for their training tasks. 2. Supporting ZeRO-3 deepspeed API and fully sharded data parallel API of PyTorch, enabling efficient sharding for trillion-parameter models. 3. Offering a framework for initiating, executing, and monitoring the training of large neural networks on allocated nodes. 4. Managing resource contention by maintaining a queue for running experiments. 5. Facilitating continuous integration of machine learning development through seamless integration with GitHub and GitHub Actions. Higgsfield streamlines the process of training massive models and empowers developers with a versatile and robust toolset.
- We install all the required tools in your server (Docker, your project's deploy keys, higgsfield binary). 2. Then we generate deploy & run workflows for your experiments. 3. As soon as it gets into Github, it will automatically deploy your code on your nodes. 4. Then you access your experiments' run UI through Github, which will launch experiments and save the checkpoints.
Stars: 4.6k
Language: Jupyter Notebook
License: Apache-2.0
Install:
bash pip install higgsfield==0.0.3
View Higgsfield on GitHub · More about Higgsfield
20. Autotrain Advanced by huggingface
This project is no longer maintained. No new features will be added and bugs will not be fixed. We recommend using Axolotl, TRL, or transformers.Trainer.
AutoTrain Advanced: faster and easier training and deployments of state-of-the-art machine learning models. AutoTrain Advanced is a no-code solution that allows you to train machine learning models in just a few clicks. Please note that you must upload data in correct format for project to be created. For help regarding proper data format and pricing, check out the documentation.
NOTE: AutoTrain is free! You only pay for the resources you use in case you decide to run AutoTrain on Hugging Face Spaces. When running locally, you only pay for the resources you use on your own infrastructure.
Stars: 4.6k
Language: Python
License: Apache-2.0
View Autotrain Advanced on GitHub · More about Autotrain Advanced
21. Libero by Lifelong-Robot-Learning
LIBERO is designed for studying knowledge transfer in multitask and lifelong robot learning problems. Successfully resolving these problems require both declarative knowledge about objects/spatial relationships and procedural knowledge about motion/behaviors. LIBERO provides: a procedural generation pipeline that could in principle generate an infinite number of manipulation tasks. 130 tasks grouped into four task suites: LIBERO-Spatial, LIBERO-Object, LIBERO-Goal, and LIBERO-100. The first three task suites have controlled distribution shifts, meaning that they require the transfer of a specific type of knowledge. In contrast, LIBERO-100 consists of 100 manipulation tasks that require the transfer of entangled knowledge. LIBERO-100 is further splitted into LIBERO-90 for pretraining a policy and LIBERO-10 for testing the agent's downstream lifelong learning performance. five research topics. three visuomotor policy network architectures. three lifelong learning algorithms with the sequential finetuning and multitask learning baselines.
Please run the following commands in the given order to install the dependency for LIBERO.
We provide high-quality human teleoperation demonstrations for the four task suites in LIBERO. To download the demonstration dataset, run:
Stars: 2.4k
Language: Jupyter Notebook
License: MIT
Install:
bash git clone https://github.com/Lifelong-Robot-Learning/LIBERO.git
View Libero on GitHub · More about Libero
22. Nemotron by NVIDIA-NeMo
Open and efficient models for agentic AI. Training recipes, deployment guides, and use-case examples for the Nemotron family.
Nemotron 3.5 Lightning is now released, a 30B-A3B hybrid Mamba-Transformer MoE with Multi-Token Prediction, built for the high-volume execution layer of long-running agents. See the release blog, the training recipe, and the model weights.
Nemotron 3 Ultra was announced at GTC San Jose 2026\. The model is open-source on Hugging Face, and the training recipe is now available in this repo\. To learn more, see the usage guide\!
Stars: 2.1k
Language: Jupyter Notebook
License: Apache-2.0
Final thoughts on learning GitHub repos
The best learning repo is the one you come back to. Pick 2 or 3 from this list, give them a month, and keep the ones you look forward to.
Navid Moazzez
AI business strategist & AI OS builder
Navid Moazzez helps creators and founders master AI and build their own AI Operating System (AI OS) to automate their business and life.
Navid.me is reader-supported. When you buy through links on this site, I may earn an affiliate commission. Learn more.
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