Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
llms 大模型 笔记50篇 此仓库包含关于机器学习、深度学习、计算机视觉、自然语言处理、大模型 爬虫等领域 项目实战
| Date | Stars |
|---|---|
| 2026-07-24 | 1108 |
| 2026-07-25 | 1108 |
| 2026-07-28 | 1108 |
| 2026-07-30 | 1108 |
| 2026-07-31 | 1109 |
| 2026-08-06 | 1109 |
Today
— stars today
This week
+1 stars this week
This month
— stars this month
Momentum
1.0
growth rate 0.09%/day
<div align="center"> <img src="assets/1.png" width="100%" alt="DL-Hub — Deep Learning from Scratch" /> # DL-Hub **从零手写，循序渐进 — PyTorch 深度学习统一学习项目** <br/> [](https://python.org) [](https://pytorch.org) [](https://numpy.org) [](https://zread.ai/skygazer42/DL-Hub) [](LICENSE) <br/> <code>339 Lessons</code> · <code>8 Learning Tracks</code> · <code>31 ML Algorithms</code> · <code>8000+ Model Zoo Architectures</code> · <code>393 Test Files</code> <br/> 统一代码风格、统一训练脚手架、统一运行方式<br/> 让学习者真正能 **"循序渐进跑通 → 改得动 → 能验收"** [Quick Start](#-quick-start) · [Learning Tracks](#-learning-tracks) · [Model Zoo](#-model-zoo) · [Federated Zoo](#-federated-learning-zoo) · [ML Algorithms](#-numpy-ml-algorithms) · [Docs](#-documentation) </div> #### Topic Coverage / 主题覆盖闭环 > The user-provided topic pool is represented by a checked code registry instead of README-only claims. `dlhub/topic_coverage.py` maps every requested topic to concrete artifacts, while `dlhub/research_streams.py`, `dlhub/framework_adapters.py`, and `dlhub/method_kits.py` cover research streams, optional frameworks, and cross-cutting methods. ```bash python -m pytest tests/test_topic_coverage.py ``` | 闭环层 | 代码入口 | |------|--------| | Topic manifest / 主题清单 | `dlhub/topic_coverage.py` | | Paper/resource/survey streams / 论文资源综述流 | `dlhub/research_streams.py` | | Framework probes / 框架探测 | `dlhub/framework_adapters.py` | | NAS/AutoML/pruning/distillation/SLAM kits | `dlhub/method_kits.py` | | Regression test / 回归测试 | `tests/test_topic_coverage.py` | ## What You'll Build <table> <tr> <td align="center" width="25%"> <br/> <b>Vision</b><br/> <sub>从 LeNet 到 ViT，<br/>791 架构 · 图像分类 / 检测 / 分割</sub> </td> <td align="center" width="25%"> <br/> <b>NLP</b><br/> <sub>从词嵌入到 Transformer，<br/>814 架构 · 分类 / NER / 阅读理解</sub> </td> <td align="center" width="25%"> <br/> <b>GNN</b><br/> <sub>从 GCN 到 PinSAGE，<br/>图分类 / 节点嵌入 / 推荐</sub> </td> <td align="center" width="25%"> <br/> <b>Point Cloud</b><br/> <sub>从 PointNet 到 PCT，<br/>64 架构 · 分类 / 部件分割 / 重建 / 15 种自监督</sub> </td> </tr> <tr> <td align="center" width="25%"> <br/> <b>Generative</b><br/> <
Excerpt of 97,902 characters
Read on GitHub
304
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:3355ebecb4539774, topic:nlp