Dive Into DL PyTorch
PyTorch port of the classic 'Dive into Deep Learning' book, with code and Chinese explanations.
An excellent free resource for PyTorch learners who read Chinese, faithfully translating the well-regarded D2L book. However, it lacks the polish and interactive features of the official D2L site, and updates depend on a single contributor.
- Chinese-speaking deep learning beginners with PyTorch preference
- Students who already know ML theory and want practical PyTorch examples
- Self-learners seeking a structured deep learning curriculum in Chinese
- Developers transitioning from MXNet to PyTorch
- Learners who prefer English-only material
- Users seeking interactive cloud notebooks or GPU access
- Advanced practitioners needing cutting-edge research implementations
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
In short
Dive Into DL PyTorch — PyTorch port of the classic 'Dive into Deep Learning' book, with code and Chinese explanations. Best for Chinese-speaking deep learning beginners with PyTorch preference, Students who already know ML theory and want practical PyTorch examples, Self-learners seeking a structured deep learning curriculum in Chinese. Free to use.
Viability Score
How likely is Dive Into DL PyTorch to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Full PyTorch implementation of all D2L chapters
- Bilingual Chinese-English explanations
- Executable Jupyter notebooks
- Coverage from basic regression to advanced GANs and transformers
- Code formatted for readability and reproducibility
- Links to original MXNet version for cross-reference
- Active maintenance by a single maintainer
- Open source under permissive license
- Companion blog with additional ML content
- Direct download of notebooks for offline use
About Dive Into DL PyTorch
Dive Into DL PyTorch is a community-powered project that reimplements the popular 'Dive into Deep Learning' (D2L) textbook, originally authored in MXNet, using PyTorch. It provides both the translated Chinese text and executable PyTorch code for each chapter, covering topics from basics (linear regression, softmax) to advanced (CNNs, RNNs, GANs, transformers). The project is maintained by TangShusen, an ML engineer with experience at Microsoft, Tencent, and Xiaohongshu. The resource is ideal for Chinese-speaking learners who want to learn deep learning hands-on but prefer PyTorch over MXNet. Each notebook includes theory explanations, code snippets, and exercises. The project also offers links to the original D2L English version and the author's LeetCode solutions. What sets it apart is its focus on bridging the gap between the original D2L's MXNet-based content and the PyTorch ecosystem, making it accessible to a wider audience. It's not a commercial product but a free, open educational resource. The project is available via GitHub and the author's blog. It does not include interactive environments like Colab badges, but users can run notebooks locally after installing dependencies.
Behind the Verdict
Dive Into DL PyTorch is a noble effort that fills a real gap for Chinese-speaking learners who want to use PyTorch with the D2L curriculum. The code is clean and well-organized, and the blog provides additional context. However, because it's a one-person side project, don't expect frequent updates or interactive features. If you can read Chinese and want a free PyTorch companion to D2L, this is a great starting point. For the most up-to-date content, consider the official D2L English version with PyTorch support or Amazon's SageMaker notebooks.
Researching Dive Into DL PyTorch? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Use Cases
- Follow along with the D2L book while writing PyTorch code instead of MXNet
- Reference PyTorch implementations for common deep learning architectures like ResNet and Transformer
- Use as supplementary material for university courses that teach deep learning with PyTorch
- Quickly look up PyTorch equivalents of MXNet code found in the original D2L
Limitations
- The project is essentially a static mirror of the original D2L with PyTorch code.
- It does not offer interactive Colab notebooks, no built-in GPU support, and content updates rely on the maintainer's availability.
- Some chapters may lag behind the latest D2L editions.
Tools that pair well with Dive Into DL PyTorch
Common stack mates teams adopt alongside Dive Into DL PyTorch, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Alternatives to Dive Into DL PyTorch
View allFrequently Asked Questions
Categories
Topics
Used Dive Into DL PyTorch? Help shape our editorial sentiment research.