Dive into Deep Learning (Chinese Edition) — User Guide

A reader's handbook for 动手学深度学习 (d2l-zh, version 2.0.0): how to install the environment, choose a deep-learning framework, run the code, and navigate the 19-chapter textbook.

1. Quick Start

d2l-ai/d2l-zh is the simplified-Chinese edition of the open textbook Dive into Deep Learning (动手学深度学习, D2L). It is authored by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola, and is published in print by Cambridge University Press (2023). The repository pins release 2.0.0, which corresponds to the second Chinese edition at zh.d2l.ai.

Unlike a typical software project, the repository is itself a book. Every chapter is a Markdown file that is also executable as a Jupyter notebook, so every algorithm the book describes can be run, modified, and inspected. Code samples exist in four parallel framework implementations — Apache MXNet, PyTorch, TensorFlow, and PaddlePaddle — and a small helper library, d2l, hides the framework-specific boilerplate behind a uniform API.

Key features

• Four frameworks, one codebase — each chapter renders parallel implementations selectable by tab
• Executable textbook — every .md chapter doubles as a Jupyter notebook via the d2lbook toolchain
• Unified helper library — from d2l import torch as d2l gives you plotting, data loading, and training utilities
• 19 chapters covering linear networks through Transformers and BERT
• Multi-format output — HTML site, framework-specific PDFs, notebook ZIPs, Colab/SageMaker mirrors
• Chinese-English parallel — _origin.md files maintain translation alignment with d2l-ai/d2l-en

Prerequisites

• Python 3.9 (Miniconda recommended)
• One deep-learning framework: PyTorch, TensorFlow, MXNet, or PaddlePaddle

Fastest path to running code

conda create --name d2l python=3.9 -y
conda activate d2l
pip install torch==1.12.0 torchvision==0.13.0
pip install d2l==0.17.6
mkdir d2l-zh && cd d2l-zh
curl https://zh-v2.d2l.ai/d2l-zh-2.0.0.zip -o d2l-zh.zip
unzip d2l-zh.zip && rm d2l-zh.zip
cd pytorch
jupyter notebook

Expected output:

[I 10:32:14.812 NotebookApp] Serving notebooks from local directory: /.../d2l-zh/pytorch
[I 10:32:14.812 NotebookApp] Jupyter Notebook is running at:
[I 10:32:14.812 NotebookApp] http://localhost:8888/?token=...

Open chapter_preliminaries/ndarray.ipynb and press Shift + Enter to execute the first code cell. If tensors print without errors, the environment is ready.

Tip

If you just want to read without installing anything, visit zh.d2l.ai. Come back to this guide when you want to run the code. Google Colab mirrors are also available for each framework — no local setup needed.

What to try next

• See §2 for full installation instructions across all platforms and frameworks.
• See §4 for how to use the d2l helper library in your own notebooks.
• See §5 for suggested reading paths and common tasks.

2. Installation

The repository's chapter_installation/index.md is the canonical install guide; what follows is a condensed, platform-by-platform distillation.

2.1 System requirements

The setup.py file declares the d2l helper library works on Python 3.5 or newer, but the rest of the book's code targets modern Python. Use Python 3.9.

Important

The CV and NLP chapters (chapter_computer-vision/, chapter_natural-language-processing-*/) fine-tune networks on real datasets. On CPU, a single epoch of ResNet-18 on CIFAR-10 can take over an hour. Secure a GPU before reaching chapter 7 (Modern CNNs) if you plan to run everything yourself.

2.2 Install Miniconda

The book standardizes on Conda-managed Python.

macOS

# For Intel Macs (Apple Silicon users: download the arm64 installer instead)
sh Miniconda3-py39_4.12.0-MacOSX-x86_64.sh -b
~/miniconda3/bin/conda init

Linux

sh Miniconda3-py39_4.12.0-Linux-x86_64.sh -b
~/miniconda3/bin/conda init

Windows

Download the .exe installer from conda.io/en/latest/miniconda.html and run it. Open the Anaconda Prompt afterwards; conda is available there without PATH changes.

Close and reopen the shell, then create the d2l environment:

conda create --name d2l python=3.9 -y
conda activate d2l

2.3 Install a deep-learning framework

The book supports four frameworks. Pick one; you do not need all four. The PyTorch tab is the most actively discussed in the community.

PyTorch

pip install torch==1.12.0
pip install torchvision==0.13.0

TensorFlow

pip install tensorflow==2.8.0
pip install tensorflow-probability==0.16.0

MXNet

# CPU-only
pip install mxnet==1.7.0.post1

# CUDA 10.1 (Linux/macOS)
pip install mxnet-cu101==1.7.0

# CUDA 10.1 on Windows
pip install mxnet-cu101==1.7.0 -f https://dist.mxnet.io/python

PaddlePaddle

# CPU-only (using the Tsinghua mirror, fast inside mainland China)
python -m pip install paddlepaddle==2.3.2 -i https://pypi.tuna.tsinghua.edu.cn/simple

# CUDA 11.2
python -m pip install paddlepaddle-gpu==2.3.2.post112 \
  -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html

Warning

Apache MXNet's upstream maintenance has wound down. The pinned mxnet==1.7.0 is an older release; newer OS + driver combinations may fail to install. If you are starting fresh and have no other constraint, choose PyTorch or PaddlePaddle instead.

2.4 Install the d2l helper library

pip install d2l==0.17.6

Important

The version 0.17.6 published to PyPI is the one the chapter_installation page of the book pins. The helper library is framework-agnostic — it imports only jupyter, numpy, matplotlib, requests, and pandas — and delegates tensor operations to whichever framework you already installed.

2.5 Download the notebooks

The book's source Markdown is converted to Jupyter notebooks by the d2lbook toolchain. The ready-made ZIP contains those notebooks already built, one subdirectory per framework.

PyTorch

mkdir d2l-zh && cd d2l-zh
curl https://zh-v2.d2l.ai/d2l-zh-2.0.0.zip -o d2l-zh.zip
unzip d2l-zh.zip && rm d2l-zh.zip
cd pytorch

TensorFlow

mkdir d2l-zh && cd d2l-zh
curl https://zh-v2.d2l.ai/d2l-zh-2.0.0.zip -o d2l-zh.zip
unzip d2l-zh.zip && rm d2l-zh.zip
cd tensorflow

MXNet

mkdir d2l-zh && cd d2l-zh
curl https://zh-v2.d2l.ai/d2l-zh-2.0.0.zip -o d2l-zh.zip
unzip d2l-zh.zip && rm d2l-zh.zip
cd mxnet

PaddlePaddle

mkdir d2l-zh && cd d2l-zh
curl https://zh-v2.d2l.ai/d2l-zh-2.0.0.zip -o d2l-zh.zip
unzip d2l-zh.zip && rm d2l-zh.zip
cd paddle

If unzip is missing on Debian/Ubuntu, install it first:

sudo apt install unzip

2.6 Verify the install

conda activate d2l
python -c "import d2l; print(d2l.__version__)"

Expected output:

0.17.6

Then, to confirm your chosen framework works end to end:

from d2l import torch as d2l         # or: from d2l import mxnet as d2l
x = d2l.tensor([1.0, 2.0, 3.0])
print(x)

If the line prints a tensor object, you are done.

• [x] Conda environment d2l created with Python 3.9.
• [x] One of torch / tensorflow / mxnet / paddlepaddle installed.
• [x] d2l==0.17.6 installed.
• [x] Notebooks downloaded and unzipped.
• [x] python -c "import d2l" succeeds.

3. Configuration

3.1 Choosing a framework

The book supports four frameworks because each has pedagogical strengths. For a new reader:

You can switch frameworks mid-book by redownloading the ZIP and extracting a different subdirectory, but your prior notebook outputs will not transfer. The surrounding prose — the book — does not change.

Tip

If you are preparing a class, standardize on one framework for the cohort. The tabbed code is designed for parity, but students comparing notes across frameworks can still run into minor numerical differences (different RNG seeds, different dropout implementations).

3.2 The d2l import convention

The import convention is identical across frameworks, thanks to the d2l package's aliasing layer:

# Choose exactly one
from d2l import mxnet as d2l        # MXNet backend
from d2l import torch as d2l        # PyTorch backend
from d2l import tensorflow as d2l   # TensorFlow backend
from d2l import paddle as d2l       # PaddlePaddle backend

The same call names work under all four backends — under the hood, the build system rewrites them into native framework operations using the alias maps declared in config.ini. For example, d2l.matmul(X, w) becomes np.dot(X, w) in the MXNet build, torch.matmul(X, w) in the PyTorch build, and tf.matmul(X, w) in the TensorFlow build.

3.3 Dataset caching

The d2l helper library downloads datasets on demand and caches them under ~/.d2l/. For example, d2l.load_data_fashion_mnist(batch_size=256) fetches Fashion-MNIST from http://d2l-data.s3-accelerate.amazonaws.com/ the first time it is called. Subsequent calls read from the cache.

If your network blocks S3, download the dataset ZIPs once by hand and place them under ~/.d2l/ before running the notebook. The URL and expected SHA-1 hash appear in the d2l/<framework>.py file's DATA_HUB dictionary.

Note

Inside mainland China, mirror services and Kaggle-hosted copies of Fashion-MNIST, CIFAR-10, and the time-machine text sometimes load faster than S3. Pre-warm the cache before a long study session.

3.4 Distribution formats

The same source repository is rendered into four consumable formats:

For full configuration reference (build config, alias system, CI settings), see the Admin Guide.

4. Core Usage

4.1 Repository structure at a glance

Note

The Markdown files in chapter_*/ are not ordinary prose. They contain :label:, :eqlabel:, :numref:, and :begin_tab: / :end_tab: directives from the d2lbook toolchain, plus #@tab <framework> annotations that select which code variant runs for a given build.

4.2 Launch Jupyter

From the framework subdirectory of the unzipped notebooks:

jupyter notebook

Jupyter prints a URL like http://localhost:8888/?token=... and opens the browser automatically. On a headless server, open that URL manually in a local browser or add --ip=0.0.0.0 --no-browser and tunnel over SSH.

Important

Re-run conda activate d2l in every new terminal session before launching Jupyter. Without it, the wrong Python interpreter will be used and imports will fail with ModuleNotFoundError: No module named 'd2l'.

4.3 Run a cell

Click any code cell and press Shift + Enter. The cell executes; the output appears beneath. A * in the brackets (In [*]) means the cell is still running.

4.4 The d2l helper library

After the import you have access to a shared vocabulary of utility functions and classes:

Note

The d2l package's source of truth lives in d2l/<framework>.py. Those files are auto-generated by the book's build pipeline from the chapters themselves — every function marked #@save in a chapter is extracted into the library. This is why reading a chapter is enough to understand where a helper came from.

4.5 Switch between code tabs inside a chapter

Each chapter's source .md contains blocks wrapped in :begin_tab:<framework> / :end_tab: directives. The pre-built notebooks contain only the cells for the framework variant you unzipped. To see the alternative implementation of a given algorithm, open chapter_*/xxx_origin.md (the English source) and search for the :begin_tab: blocks, or visit the corresponding section on zh.d2l.ai and use the framework-switch button in the navbar.

4.6 Hardware guidance

Cloud options the book documents:

• AWS EC2 — chapter_appendix-tools-for-deep-learning/aws.md walks through launching a Deep Learning AMI instance.
• AWS SageMaker — chapter_appendix-tools-for-deep-learning/sagemaker.md covers creating a notebook instance.
• Google Colab — the book's per-framework Colab mirrors host ready-to-run notebooks with free GPU/TPU tiers.
• Server + GPU selection — chapter_appendix-tools-for-deep-learning/selecting-servers-gpus.md compares T4 / V100 / A100 class hardware.

Tip

For the Kaggle competitions inside chapter_computer-vision/kaggle-cifar10.md and kaggle-dog.md, Kaggle's own hosted kernels (free GPU, dataset pre-mounted) are often the simplest option.

4.7 Cleanly stop your session

Stop the Jupyter server with Ctrl + C in the terminal where you launched it. Then:

conda deactivate

5. Common Tasks

5.1 The book's structure

The book is organized in three macro-parts.

Part I — Foundations (chapters 1-4)

Part II — Modern Deep Learning (chapters 5-11)

Part III — Advanced Topics and Applications (chapters 12-15)

Appendix

The contrib/ folder

contrib/chapter_recommender-systems/ contains draft material on recommender systems (AutoRec, factorization machines, matrix factorization, MovieLens, CTR prediction) that is not yet part of the main book. contrib/to-rm-mx-contrib-text/ is a retired MXNet-era NLP chapter kept for archival.

Caution

Contents under contrib/ are excluded from the website build (see config.ini exclusions). They may be out of date or incomplete.

5.2 Suggested reading paths

Complete traversal (≈ one semester): Read straight through from chapter_preface to chapter_natural-language-processing-applications. Run every notebook; skip no exercises. Expect 12-16 weeks.

"Skip the math" fast path (≈ four weeks):

1. chapter_introduction/index.md — motivation
2. chapter_preliminaries/ndarray.md + autograd.md — tensor & autograd mechanics
3. chapter_linear-networks/linear-regression-concise.md — the shortest working training loop
4. chapter_multilayer-perceptrons/mlp-concise.md — nonlinearity
5. chapter_convolutional-neural-networks/lenet.md — the first real CNN
6. chapter_convolutional-modern/resnet.md — the workhorse architecture
7. chapter_recurrent-modern/seq2seq.md — sequence generation
8. chapter_attention-mechanisms/transformer.md — the Transformer

Skipping the from-scratch sections halves the time but hides how the abstractions work. Return to them later.

NLP-focused path: chapter_preliminaries/ → chapter_linear-networks/ → chapter_multilayer-perceptrons/ → chapter_recurrent-neural-networks/ → chapter_recurrent-modern/ → chapter_attention-mechanisms/ → chapter_natural-language-processing-pretraining/ → chapter_natural-language-processing-applications/.

Computer-vision-focused path: chapter_preliminaries/ → chapter_linear-networks/ → chapter_multilayer-perceptrons/ → chapter_deep-learning-computation/ → chapter_convolutional-neural-networks/ → chapter_convolutional-modern/ → chapter_computer-vision/.

5.3 Run a single notebook from the command line

conda activate d2l
jupyter nbconvert --to notebook --execute \
  chapter_linear-networks/linear-regression-concise.ipynb \
  --output executed_linear-regression-concise.ipynb

Expected output:

[NbConvertApp] Converting notebook chapter_linear-networks/linear-regression-concise.ipynb to notebook
[NbConvertApp] Writing 124578 bytes to chapter_linear-networks/executed_linear-regression-concise.ipynb

5.4 Convert the book's Markdown to a notebook yourself

The release ZIP already contains .ipynb files. If you prefer to work from the raw Markdown chapters in chapter_*/*.md, the d2lbook toolchain converts them:

pip install d2lbook
d2lbook build eval

This produces notebooks under _build/eval/. Each .md becomes one .ipynb; the #@tab <framework> annotations select which code cells survive.

Note

d2lbook is the build tool used internally by the authors. Most readers do not need it — the pre-built notebook ZIP is the official distribution channel. Building locally is only interesting if you want to modify content and re-render the output.

5.5 Print a quick plot with the book's helper

from d2l import torch as d2l
import torch

x = torch.arange(-5, 5, 0.1)
d2l.plot(x, [torch.sin(x), torch.cos(x)], 'x', 'f(x)', legend=['sin', 'cos'])

This produces an SVG plot directly inside the notebook. d2l.plot auto-sets figsize, the matplotlib SVG backend, and axis grids to the book's style.

5.6 Join the discussion forum

Each section of the book ends with a link like [Discussions](https://discuss.d2l.ai/t/2082). Those numeric IDs point to a dedicated forum thread for that section. If something is unclear, post there; the authors and veteran readers respond.

5.7 Glossary (key terms)

The full English↔Chinese glossary lives in TERMINOLOGY.md. Key terms the book uses consistently:

See TERMINOLOGY.md for the complete list (100+ entries).

6. Troubleshooting

Error: ModuleNotFoundError: No module named 'd2l'

Cause: the d2l environment is not active, or d2l==0.17.6 was installed into a different environment.

Solution:

conda activate d2l
pip show d2l | grep Version

Expected output:

Version: 0.17.6

If the version differs, reinstall: pip install --force-reinstall d2l==0.17.6.

Prevention: always open a fresh terminal, run conda activate d2l, then launch Jupyter.

Error: No module named 'mxnet' / 'torch' / 'tensorflow' / 'paddle'

Cause: the framework that matches the subdirectory you unzipped (mxnet/, pytorch/, tensorflow/, or paddle/) is not installed in the active environment.

Solution: install the framework listed at the top of the failing notebook (see §2.3) in the same environment where d2l lives.

Prevention: decide on a framework first, install exactly that one, then cd into the matching subdirectory.

Error: AssertionError: Torch not compiled with CUDA enabled when calling try_gpu()

Cause: PyTorch was installed from the default PyPI wheel without CUDA, but the notebook is trying to move a tensor to GPU.

Solution: either install a CUDA-enabled PyTorch wheel from pytorch.org/get-started/locally that matches your CUDA driver, or force the code onto CPU by calling device = d2l.try_gpu() which falls back to cpu() when CUDA is unavailable. On CPU the run will be slow but correct.

Prevention: before installing a framework, check your CUDA driver: nvcc --version. Install the matching wheel.

Warning

CUDA version mismatches between the driver and the PyTorch wheel are the single most common installation failure. Always verify nvidia-smi (driver CUDA) and nvcc --version (toolkit CUDA) show compatible versions before installing a GPU wheel.

Error: CUDA out of memory during training

Cause: the batch size in the notebook exceeds what your GPU can hold.

Solution:

# Typical book default
batch_size = 256

# Halve until it fits
batch_size = 128

Also close other notebooks that hold GPU tensors.

Prevention: monitor GPU usage with nvidia-smi before starting a training run; leave at least ~1 GB free.

Error: UnicodeEncodeError when printing Chinese strings

Cause: the terminal encoding is not UTF-8 (Windows default).

Solution:

# In the Anaconda Prompt
chcp 65001
set PYTHONIOENCODING=utf-8

Then relaunch the notebook server.

Prevention: on Windows, prefer running Jupyter from the Anaconda Prompt with UTF-8 enabled, or use WSL.

Error: ValueError: zero-size array to reduction operation fmin which has no identity on Apple Silicon

Cause: MXNet 1.7 does not run natively on Apple Silicon; PyTorch older than 1.12 requires Rosetta.

Solution: switch to the PyTorch or TensorFlow tab, and use a recent build (torch>=2.0). The book's pinned versions were released before the M-series Macs; later versions work with the book's code.

Prevention: on Apple Silicon, skip MXNet. PyTorch MPS backend is the smoothest path.

Error: notebook downloads stall on d2l-data.s3-accelerate.amazonaws.com

Cause: firewall or slow international bandwidth.

Solution: manually download the dataset ZIP (URLs are inside d2l/<framework>.py's DATA_HUB dict) and place it in ~/.d2l/ before running the cell that needs it. Inside mainland China, mirror services and Kaggle-hosted copies of Fashion-MNIST, CIFAR-10, and the time-machine text sometimes load faster.

Prevention: pre-warm the cache before a long study session.

Error: matplotlib figures render as static PNGs, not SVGs

Cause: the matplotlib inline backend was not reset to SVG.

Solution: call d2l.use_svg_display() at the top of the notebook, or re-import with from d2l import torch as d2l which runs use_svg_display() on import via the first d2l.plot() call.

Prevention: always from d2l import <framework> as d2l before creating figures.

Error: IndexError: index ... out of bounds when iterating data_iter

Cause: the dataset was partially downloaded and is corrupt.

Solution: delete the ~/.d2l/ cache and rerun the notebook; the helper library re-downloads and re-verifies.

Prevention: do not kill the download cell midway on the first run.

Error: RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation

Cause: a framework-specific in-place op conflicts with autograd. Most often this appears when readers adapt a from-scratch chapter and accidentally use += on a parameter.

Solution: replace w += grad with w = w - lr * grad (out-of-place) or wrap the update in with torch.no_grad(): / param[:] = ....

Prevention: study chapter_linear-networks/linear-regression-scratch.md carefully — its sgd() is the template for correct in-place parameter updates.

Caution

In-place parameter updates (+=, -=) during gradient computation will silently corrupt the autograd graph in PyTorch. The resulting errors may not appear until several training iterations later, making them difficult to debug. Always use with torch.no_grad(): for manual parameter updates.

Error: figures show boxes instead of Chinese characters

Cause: the system font cache does not include a CJK font.

Solution: on Linux, install Noto CJK: sudo apt install fonts-noto-cjk. On macOS, PingFang is installed by default. On Windows, install Source Han Sans SC from adobe-fonts/source-han-sans.

Prevention: this only affects rebuilding the book's PDF yourself; the pre-rendered PDFs at zh-v2.d2l.ai already contain the correct fonts.

Error: d2l.train_ch3: TypeError: ... got an unexpected keyword argument

Cause: you are using a d2l package version that does not match the chapter you are reading. Chapters 3-5 and chapters 13+ evolved their helper signatures.

Solution: reinstall the pinned version: pip install --force-reinstall d2l==0.17.6. Make sure the book content you are reading is the 2.0.0 release.

Prevention: pin the version; do not pip install -U d2l.

Error: ImportError: cannot import name 'npx' from 'mxnet'

Cause: the MXNet version is newer than 1.7.0; the npx namespace layout changed.

Solution: pip install mxnet==1.7.0.post1 (CPU) or pip install mxnet-cu101==1.7.0 (CUDA 10.1).

Prevention: use the exact versions in chapter_installation/index.md.

Error: Jupyter kernel dies on first import torch

Cause: an incompatible CUDA/driver combination, or low memory.

Solution: rerun outside Jupyter: python -c "import torch; print(torch.__version__)". The Python error message will pinpoint the issue. Common fixes: upgrade the driver, install the CPU wheel, or add swap.

Prevention: verify the framework imports cleanly from a plain python shell before using it in Jupyter.

Error: exercises reference a figure that does not exist in the notebook

Cause: the exercise references a later section; the figure lives in a subsequent notebook.

Solution: read the online HTML for navigation — the website links figure references across chapters. The standalone .ipynb files do not have cross-notebook hyperlinks.

Prevention: keep zh.d2l.ai open in a browser tab alongside the local notebooks.

7. FAQ

Q: Which edition of the book does this repository correspond to?

Version 2.0.0, the second edition of the Chinese book, matching zh.d2l.ai. The first edition lives at zh-v1.d2l.ai and in the repository's historical tags.

Q: Is the English version in the same repository?

No. d2l-ai/d2l-en is the upstream English source; d2l-ai/d2l-zh is the Chinese translation. Paragraph-level English originals are retained as _origin.md files inside each chapter folder for translators to keep in sync.

Q: Do I need a GPU to read the book?

Not for the first six chapters (chapter_preface/ through chapter_multilayer-perceptrons/). From chapter_convolutional-modern/ onward, a GPU is strongly recommended.

Q: Can I run everything on a Mac?

Yes, using the PyTorch tab. On Apple Silicon, PyTorch's MPS backend accelerates many (not all) ops. MXNet does not support Apple Silicon well — choose a different framework.

Q: Which framework gets the most maintenance?

PyTorch and PaddlePaddle are the most actively updated in this repository. TensorFlow is maintained. MXNet's upstream maintenance has wound down; the book's MXNet path still works against the pinned versions but is not a great choice for a fresh reader.

Q: How do I cite the book in a paper?

The README.md provides a BibTeX entry:

@book{zhang2023dive,
    title={Dive into Deep Learning},
    author={Zhang, Aston and Lipton, Zachary C. and Li, Mu and Smola, Alexander J.},
    publisher={Cambridge University Press},
    note={\url{https://D2L.ai}},
    year={2023}
}

Q: Is there a paid version?

There is a print edition published by Cambridge University Press (人民邮电出版社 in mainland China). The online edition, PDFs, and notebooks are free.

Q: Can I use the code in my own projects?

Yes. The code is MIT-0 licensed (setup.py, config.ini); the book content is CC-BY-SA-4.0. Attribute the authors and inherit share-alike for derivative written work.

Q: My notebook shows different loss numbers than the book — is that a bug?

Most likely not. The book's numbers are from a specific random seed, framework version, and hardware. Expect variance in the third decimal place between runs. If your numbers are orders of magnitude off, re-check batch size, learning rate, and framework version.

Q: Where are the answers to the exercises?

There is no official answer key — the exercises are pedagogical. Post your solutions on the discussion forum link at the end of each section (discuss.d2l.ai).

Q: Can I skip the "from scratch" versions and read only the "concise" ones?

You can, and your reading time will drop by roughly half. But you will understand the abstractions far better if you read both pairs.

Q: How do I report a typo?

Edit the Markdown file directly on GitHub (click the pencil icon on any file) and open a pull request. See the Admin Guide for the full contributor workflow.

Q: What if I want to translate the book into another language?

Fork d2l-ai/d2l-en and follow its translation conventions. The Chinese edition is one such fork. The _origin.md dual-file layout used here is the recommended pattern for keeping a translation in sync with upstream.

Q: Does the book cover generative models / diffusion / LLMs?

The 2.0.0 release covers up to Transformers and BERT pre-training. Diffusion models and larger-scale generative architectures are tracked in the English upstream d2l-ai/d2l-en for later editions; they are not yet in the Chinese release.

Q: Can I run notebooks in VS Code instead of Jupyter?

Yes. Install the Jupyter extension for VS Code, select the d2l conda environment as the kernel, and open any .ipynb file. All plots render inline, same as the browser UI.

Q: Why does config.ini list libs = ... -U mxnet-cu101==1.7.0?

That is the Colab install command used when the book's Colab mirrors build their notebooks. The -U forces an upgrade over Colab's pre-installed MXNet. You do not need to run that command locally.

8. Additional Resources

• Official site (Chinese v2): zh.d2l.ai
• First edition (legacy Chinese): zh-v1.d2l.ai
• English upstream: github.com/d2l-ai/d2l-en
• Discussion forum: discuss.d2l.ai/c/chinese-version/16
• Berkeley STAT 157 course materials: courses.d2l.ai/berkeley-stat-157
• Chinese course site: courses.d2l.ai/zh-v2
• Colab per framework:
  • MXNet — github.com/d2l-ai/d2l-zh-colab
  • PyTorch — github.com/d2l-ai/d2l-zh-pytorch-colab
  • TensorFlow — github.com/d2l-ai/d2l-zh-tensorflow-colab
  • PaddlePaddle — github.com/d2l-ai/d2l-zh-paddle-colab
• SageMaker per framework: d2l-ai/d2l-zh-sagemaker, d2l-zh-pytorch-sagemaker, d2l-zh-tensorflow-sagemaker, d2l-zh-paddle-sagemaker
• Slides (PyTorch): github.com/d2l-ai/d2l-zh-pytorch-slides
• Release archives on S3:
  • s3://d2l-webdata/releases/d2l-zh/d2l-zh-1.0.zip
  • s3://d2l-webdata/releases/d2l-zh/d2l-zh-1.1.zip
  • s3://d2l-webdata/releases/d2l-zh/d2l-zh-2.0.0.zip