OrganizationDatawhale
@datawhalechina • for the learner，和学习者一起成长. Use this route to separate flagship concentration from portfolio breadth before you treat a publisher as broadly strong.
Portfolio concentration
70%
Top three share
Shows whether the organization is driven by one breakout repo or several visible projects.
Breadth
30 repos
Visible snapshot
20 repositories updated in the last 90 days.
Leading language
Python
Portfolio mix
Python (9), Unknown (8), Jupyter Notebook (6)
Average size
644
Stars per repository
Useful for distinguishing one flagship-heavy publisher from a repeatable portfolio.
Updated: 2026-10-27(18h ago)GitHub API fallback30 repositories
70%
of the visible star count comes from this organization's top three repositories.
644
stars per repository in this same snapshot.
Python
is the most common language here, with 20 repositories updated in the last 90 days.
Why this rank
This organization stands out because its public portfolio is relatively balanced across 30 repositories.
Balanced portfolio across 30 reposTop 3 share 70%
Organization pages work best when you separate portfolio breadth from flagship concentration. In Datawhale's case, the visible top three repositories account for about 70% of total stars in this snapshot, which helps explain whether the organization is known for one breakout project or for a broader repeatable portfolio.
The dominant language mix here is Python (9), Unknown (8), Jupyter Notebook (6). That makes this page useful not just for popularity checks, but also for seeing what technical shape an organization's public ecosystem actually has.
Top Repositories
| # | Repository | Language | Stars |
|---|---|---|---|
| 1 | datawhalechina/Agent-Learning-Hub AI Agent 学习路线与资料库收集 | HTML | 9.1K |
| 2 | datawhalechina/deepagents-in-action 📚 《Deep Agents 实战》—— LangChain 官方大使出品，基于 LangChain / LangGraph 生态，从零构建生产级 AI Agent 的完整指南 | Jupyter Notebook | 2.8K |
| 3 | datawhalechina/zero-to-sglang Official SGLang x Datawhale course on LLM inference: understand inference, build a mini-sglang from scratch, then read the real SGLang source and land your first PR. Available in English and Chinese. | Python | 1.6K |
| 4 | datawhalechina/llm-algo-leetcode LLM algorithm practice lab with theory, solutions, and test cases.《大模型算法与系统教程》面向大模型入门到进阶的算法实战教程，覆盖原理讲解、答案解析、测试用例与 CUDA/Triton 实战。 | Jupyter Notebook | 729 |
| 5 | datawhalechina/deep-learning-notes Personal deep learning study notes and tutorial-style notebooks | Python | 709 |
| 6 | datawhalechina/dive-into-embodied-ai 从零到一搭建一台具身智能机器人:深入强化学习、World-Model、VLA 等智能决策方法的工程落地,贯穿仿真环境、控制器、运动规划、感知系统等技能树模块,并在真实项目中跑通"决策—控制—感知"完整链路。 | Python | 645 |
| 7 | datawhalechina/quant-for-beginners | Jupyter Notebook | 644 |
| 8 | datawhalechina/hello-generic-agent 📚 《Generic Agent使用指南》——轻松上手自进化智能体，从基础调用到高级技巧全覆盖 | 624 | |
| 9 | datawhalechina/jev-cookbook ⚡ 适合中国宝宝的 Jev 入门教程｜手把手带你了解关于 Jev 的一切——Jupyter Notebook 轻松实验，从三种问题原语到 18 篇实战配方、语音智能家居、模型评测与本地微调，全面掌握 System One 判断模型的开发范式 | Jupyter Notebook | 537 |
| 10 | datawhalechina/learn-world-model Learn everything about world model | TypeScript | 484 |
| 11 | datawhalechina/Path2AGI 从基础学科到通用人工智能 - 跨学科知识图谱 | 409 | |
| 12 | datawhalechina/start-mllm 从0开始学习多模态大模型，从概念、架构、训练到部署，系统搭建你的第一套 MLLM 知识体系 | 226 | |
| 13 | datawhalechina/omni-info-radar A Flash, Omni info discovery radar for any topic with zero-cost. | Python | 146 |
| 14 | datawhalechina/hello-robot hello-robotic 将基于 G2 与 Isaac 打造一套全流程机器人仿真实践教程平台，覆盖搭建、感知、建图定位导航、抓取操作及大模型决策，同时可以用于前沿算法的实践验证 | Python | 142 |
| 15 | datawhalechina/hello-gpu Hello GPU：从 AMD GPU 架构、ROCm 工具链到 HIP/Triton 算子优化与单卡推理性能分析的实战教程。 | Jupyter Notebook | 129 |
| 16 | datawhalechina/ai-skills-for-everyone 本项目面向普通学习者和办公用户，搜罗并评审高质量开源 AI Skill，按场景提供中文教程、开箱 starter、风险说明和使用路线，帮助不会写代码的人也能低成本使用开源 AI 能力。 | HTML | 102 |
| 17 | datawhalechina/ai-prompting-for-everyone 本项目整理自吴恩达老师在 DeepLearning.AI 平台推出的 AI Prompting for Everyone 课程，主要面向中文学习者，提供课程内容翻译、知识点梳理和示例说明。 这门课程偏入门，重点不是讲复杂的模型原理，而是讲普通用户如何更好地向 AI 提问，如何把任务说清楚，如何让 AI 给出更符合需求的回答。 本项目希望把课程内容整理成更适合中文阅读的学习资料，方便大家在学习、办公、写作、编程和资料整理等场景中参考使用。 如果你正在学习 AI 提示词，或者想系统了解如何更有效地使用 ChatGPT、Gemini、Claude 等AI工具，可以从这个项目开始。 | 95 | |
| 18 | datawhalechina/mini-workbuddy 用 AgentScope 从零搭一个本地智能体工作台 | Python | 30 |
| 19 | datawhalechina/ai-wiki AI-Wiki：AI开发者全景式工具与资源导航 | Python | 28 |
| 20 | datawhalechina/learning-terrain 《学习的地形》是一部从学习动力学、几何视角重新理解深度学习的开源书籍。 | 25 | |
| 21 | datawhalechina/ai-learning-roadmap | JavaScript | 21 |
| 22 | datawhalechina/whale-tutor 🐳 AI 驱动的开源交互式学习引擎 | TypeScript | 21 |
| 23 | datawhalechina/learn-auto-research AutoResearch official style beginner tutorial, from 0 to 1 | Python | 20 |
| 24 | datawhalechina/vc-lab 本套课程，希望通过AI硬件产品的视角与产品设计的SOP探索，一起学习和研究，自己如何真正发现自己的或者别人的需求，如何设计自己的产品和项目，从而真正产出帮助到大家的产品 | 10 | |
| 25 | datawhalechina/signal-to-intelligence 《从信号到智能》从数字信号处理到大语言模型的思想演化与代码实践。面向工程师的思想对照式快速入门教程。帮你把已有的信号处理 / 统计知识迁移到现代 AI（深度学习、Transformer、LLM、多模态）的理解中。 目标读者： 具有理工科背景、学过 DSP / 控制 / 通信 / 机器学习基础，想快速理解 LLM 本质的工程师。 | Python | 8 |
| 26 | datawhalechina/dev-mesh DevMesh 是一个面向多人协作开发的 local-first 知识共享层。它通过 MCP 为 Codex、Claude Code、opencode 等 AI 编程工具提供项目知识检索、经验沉淀、本地缓存和可选团队同步能力 | TypeScript | 4 |
| 27 | datawhalechina/hello-hexo-blog | 4 | |
| 28 | datawhalechina/robo-genesis-101 | Jupyter Notebook | 3 |
| 29 | datawhalechina/anycli Built for agents,A CLI-native platform,CLI registration & indexing platform | TypeScript | 3 |
| 30 | datawhalechina/hello-ai hello-ai | 2 |
Next step after the organization read
Open a flagship repository, compare a couple of portfolio leaders, or return to the organization map when you want a broader concentration read.
Learn and methodology
Keep trust-building context reachable, but behind the first data read instead of ahead of it.
How to read this organization snapshot
Total stars are useful as a discovery signal, but they do not tell you whether a team maintains every repository equally. Pair this page with release cadence, maintainer activity, and the flagship concentration shown above before making adoption decisions.
For broader background on GitStar's ranking logic and editorial guidance, see Methodology & Editorial Standards.