// the take
A Chinese-language collection of notes and Jupyter notebooks covering AI/ML/DL theory and practice, split across math fundamentals, machine learning, deep learning, NLP, and tooling. It's aimed at Chinese-speaking students and engineers building foundational knowledge, not at engineers looking for a library or tool.
The topic coverage is genuinely broad — math fundamentals through industry applications (recommender systems, autonomous driving) — and it includes real worked exercises, like the full set of Andrew Ng Deep Learning Specialization Colab notebooks (CNNs, sequence models, optimization) rather than just prose summaries. The writing explains concepts (ANI/AGI/ASI, the ML vs DL relationship) in plain terms rather than just dumping links.
Most of the actual content lives in sibling repos (MachineLearning-Notes, NLP-Notes, DeepLearning-Notes, etc.) — several top-level folders here are just README.link stubs, so this repo alone is mostly a table of contents, not self-contained material. It's Chinese-only with no translation, which caps the audience. The Coursera/Andrew Ng notebooks appear to be copied in wholesale rather than originally authored, which is a licensing gray area given the repo's own CC BY-NC-SA claim. There's no indication of active curation or correction of outdated material — some of the historical AI narrative reads like static reference text rather than something kept current.
We read the README, the directory tree and a key source file in October 2026. Nobody pays to be listed. How we judge →