The deep learning book 500 universities use in Chinese and open source

What it does This is the Chinese edition of Dive into Deep Learning, an open-source textbook that teaches deep learning by mixing mathematical foundations with executable code. The project provides a unified resource where concepts, background knowledge, and implementation live side-by-side, aimed at readers who want to understand the math and actually build things. It is freely available online and backed by a discussion forum for technical Q&A.

The interesting bit Instead of separating theory from practice, the book embeds runnable code directly alongside the math, so you can modify a formula’s implementation and see the result immediately. The authors also treat the book as a continuously iterated product rather than a static artifact, updating content to track the field’s rapid evolution.

Key highlights

• Adopted as a textbook or reference by more than 500 universities across over 70 countries (for both Chinese and English versions).
• Published in print by Cambridge University Press, yet the full text and code remain freely accessible online.
• Covers the full stack: critical thinking, mathematical derivations, and engineering skills in one place.
• Includes companion teaching resources, such as slides and video references from a UC Berkeley introductory course.
• Community-driven updates via GitHub contributions and a dedicated Chinese-language discussion forum.

Verdict Worth bookmarking if you are a Chinese-speaking developer or student who wants to learn deep learning by breaking code, not just reading proofs. Skip it if you are already a seasoned researcher looking for frontier survey papers rather than foundational pedagogy.