D2l Zh
免费可运行的深度学习中文教科书，Jupyter笔记本边学边练
对于想扎实掌握深度学习基础的中文读者，这本书几乎是免费且最优的选择。它用可运行代码把每个概念讲透，从CNN到Transformer都有实战。第二版新增PaddlePaddle实现，框架兼容性更强。但如果你已经在做LLM微调或扩散模型，这本书的内容会显得偏旧，更适合作为查漏补缺的参考。相比吴恩达的《深度学习专项课程》更侧重代码实践，比《深度学习》（花书）更易上手。
Verified 3d ago · liveness 70/100 · cite: rightaichoice.com/tools/d2l-zh
- 深度学习初学者，希望从零开始动手实践
- 机器学习工程师，需要快速提升编码能力
- 高校教师，用作深度学习课程的教材或参考书
- 自学者，利用免费资源系统学习深度学习
- 寻求最新研究前沿（如扩散模型、LLM微调）的高级研究者
- 偏好纯理论推导而非代码实践的读者
- 不使用Python或主流深度学习框架（PyTorch/TF/Paddle）的开发者
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
Skip D2L if you are already working on LLM fine-tuning, diffusion models, or other post-2023 frontiers — this book stops around BERT/Transformer and won't keep you current.
在线版完全免费，但纸质书约100元，第一版全彩精装版约100-200元，如果只想要纸质版需付费。
在线版完全免费，纸质书约100元，比大多数编程书便宜。对于想免费入门深度学习的学生和自学者，几乎零成本。相比付费课程（如Coursera专项课程每月订阅费），D2L提供同等深度的内容且免费。
In short
D2l Zh — 免费可运行的深度学习中文教科书，Jupyter笔记本边学边练. Best for 深度学习初学者，希望从零开始动手实践, 机器学习工程师，需要快速提升编码能力, 高校教师，用作深度学习课程的教材或参考书. Free to start; paid plans from ¥100/mo.
What people actually say about D2l Zh — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
15 mentions across 1 source (Lemmy) · researched Jul 3, 2026.
- +Completely free with no paywall or subscription required.
- +Interactive Jupyter notebooks let you run code as you learn.
- +Covers both computer vision and NLP comprehensively.
- +Written by Amazon principal scientist, high pedigree.
- +Used by 500+ universities worldwide for teaching.
- −No user feedback available from scraped community data.
- −Chinese-only language may exclude non-native speakers.
- −Lack of official support channels like forums or chat.
- −No TensorFlow or JAX code examples included.
- −PDF download not mentioned in all sources.
- • No hidden costs detected; the resource is completely free.
Viability Score
How well maintained and how widely used is D2l Zh? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- 可运行的Jupyter笔记本，每节一练
- 支持PyTorch、MXNet、TensorFlow、PaddlePaddle四种框架
- 从零实现和简洁实现双轨代码
- 实战Kaggle比赛项目（房价预测、CIFAR-10分类等）
- 计算机视觉：图像分类、目标检测、语义分割
- 自然语言处理：word2vec、BERT、情感分析
- 注意力机制与Transformer详解
- 优化算法：SGD、Adam、学习率调度
- 计算性能：多GPU训练、自动并行
- 免费在线文档与PDF下载
- 配套课程视频（B站）
- 社区讨论区与200+贡献者
- 课件、作业等教学资源
- 本地、SageMaker Studio Lab、SageMaker、Colab运行
About D2l Zh
《动手学深度学习》（D2L）是一本面向中文读者的交互式深度学习教科书，由亚马逊科学家李沐等人编写。全书从线性神经网络讲到Transformer，覆盖计算机视觉、自然语言处理、优化算法等核心领域。每一节都是可运行的Jupyter笔记本，你可以随时修改代码和超参数，边学边练。第二版在线内容支持PyTorch、NumPy/MXNet、TensorFlow和PaddlePaddle四种框架，共16章，每章配有从零实现和简洁实现两套代码，并穿插多个Kaggle实战项目，如房价预测、CIFAR-10图像分类和狗的品种识别。 这本书强调“动手”，所有概念都通过可执行代码演示，而不是停留在公式和理论。你可以在本地、Amazon SageMaker Studio Lab、Amazon SageMaker或Google Colab上运行所有代码，无需复杂环境配置。社区活跃，200多位贡献者持续更新内容，你可以通过每章末尾的链接与数千名学习者讨论。配套资源包括免费在线文档、PDF下载、课件、作业和B站教学视频。 第二版纸质书《动手学深度学习（PyTorch版）》已在京东、当当上架，内容与在线版一致，但排版更规范。第一版纸质书还有全彩精装版可选。无论自学还是教学，这本书都提供了完整的知识体系和实战路径，被全球70多个国家500多所大学用作教材或参考书。 与传统的理论型教材相比，这本书更贴近工程实践，代码可以直接运行，学习曲线更平缓。它持续更新以跟随深度学习的主流进展，但注意它覆盖的是基础到中阶内容，并不是最新的研究前沿。
Behind the Verdict
当你的目标是真正理解深度学习的基本原理，并且希望亲手写代码验证每个概念时，这本书几乎是不可替代的。它把“动手”两个字贯彻到底，每一节都配有可运行的Jupyter笔记本，你可以在本地、SageMaker Studio Lab或Colab上直接跑通，这在同类教材里非常少见。 它的覆盖面很实在：从线性回归、多层感知机，到CNN、RNN、注意力机制和Transformer，再到优化算法和计算性能，每一章都有从零实现和简洁实现两套代码，让你既看懂底层逻辑，又能直接上手现成API。三个Kaggle实战项目（房价预测、CIFAR-10分类、狗的品种识别）把前面学的知识串起来，是真刀真枪的练习。 但别指望它带你冲到研究前沿。书里最“新”的内容止步于Transformer和BERT微调，扩散模型、LLM微调、RLHF这些当前热点基本没覆盖。如果你已经在训练大模型，这本书的参考价值会大打折扣，更适合当作查漏补缺的工具书。 与吴恩达的《深度学习专项课程》相比，这本书更硬核、更偏向代码实现，吴恩达的课程更偏概念和直观理解。与《深度学习》（花书）相比，它更易上手，花书偏重理论推导，而这本几乎每个公式都配有可运行代码。 要注意的是，这本书的中文版和英文版内容高度同步，但中文社区讨论更活跃，B站视频也是基于中文版录制的，所以中文读者学起来会更顺畅。另外，书里的代码有时依赖特定版本的库，环境配置可能让你卡壳，但附录里专门讲了SageMaker和EC2的用法，算是给了救急方案。 总体而言，如果你是深度学习的初学者或者想系统补基础的中级工程师，这本书是免费的、口碑扎实的选择。但如果你需要的是最新的前沿知识或者纯理论的严谨推导，建议另找其他资源。
Researching D2l Zh? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Real-world workflow fit
Concrete scenarios for the personas D2l Zh actually fits — and what changes day-one when you adopt it.
想入门深度学习，但预算有限，需要系统学习路径和实战练习。
Outcome: 用免费在线版按章节顺序学习，每节修改代码和超参数，完成CIFAR-10 Kaggle项目，掌握CNN和Transformer基础。
需要快速补充深度学习理论并提升编码能力，用于工作中的模型选型和调优。
Outcome: 跳过基础章节，直接阅读CNN、RNN、Transformer章节，运行简洁实现代码，参考Kaggle项目模板，加速实际模型开发。
需要为深度学习课程备课，准备课件、作业和实验。
Outcome: 利用官网提供的课件和作业资源，将Jupyter笔记本直接用于实验课，学生可在线运行，降低环境配置门槛。
Use Cases
- 学习深度学习基础概念和数学原理
- 动手实现卷积神经网络用于图像分类
- 使用Transformer进行机器翻译实验
- 通过Kaggle比赛项目巩固实战技能
- 备课并获取教学资料（幻灯片、习题）
- 自学深度学习，从零到可部署模型
Limitations
《动手学深度学习》覆盖基础到中阶内容，截止到Transformer和BERT，对2023年后的LLM微调、扩散模型等前沿话题几乎未涉及。代码基于较早的框架版本，可能与最新API不完全兼容，需要自行适配。内容以中文为主，英文版并行但中文更新优先。作为教材，缺少动态更新机制，无法实时反映最新研究进展。
as of 2026-08-25
Verification history
We have re-verified D2l Zh 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published D2l Zh tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
在线版
$0/mo
Ideal for
任何想免费学习深度学习的人，尤其是学生和自学者，无需付费即可访问全部内容。
What this tier adds
起始免费层，提供全部16章在线内容、可运行笔记本、四种框架支持及免费PDF下载。
第二版纸质书（PyTorch版）
约¥100
Ideal for
喜欢纸质阅读或需要权威排版的学习者，适合作为教材或收藏。
What this tier adds
相比在线版，增加了纸质印刷的便利性和规范排版，但内容基本一致。
第一版纸质书（全彩精装）
约¥100-200
Ideal for
偏好精装收藏或对视觉呈现有要求的读者，内容为第一版。
What this tier adds
相比第二版平装，提供全彩精装版，价格可能更高，内容为第一版。
Where the pricing makes sense
The company stage and team size where D2l Zh's pricing actually pencils out — and where peers do it cheaper.
在线版完全免费，纸质书约100元，比大多数编程书便宜。对于想免费入门深度学习的学生和自学者，几乎零成本。相比付费课程（如Coursera专项课程每月订阅费），D2L提供同等深度的内容且免费。
Setup time & first value
How long it actually takes to get something useful out of D2l Zh — broken out by persona, not the marketing-page minute.
在线版即时访问，无需安装，打开浏览器即可开始前几章。如果要本地运行所有代码，安装Python和Jupyter约需30分钟，配置PyTorch/TF等框架约1小时。
Switching to or from D2l Zh
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From 纯理论教材（如花书）：D2L提供可运行代码，从零实现到简洁实现，能帮助你快速上手实践。
- ↗To 前沿研究：可继续阅读论文或参与LLM微调课程，D2L作为基础。
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with D2l Zh
Common stack mates teams adopt alongside D2l Zh, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
D2l Zh vs Surge Ai
D2l Zh and Surge AI serve diametrically opposite needs. D2l Zh is a free, comprehensive textbook for learning deep learning hands-on, ideal for students and self-learners. Surge AI is a paid enterprise platform that provides expert human feedback and advanced benchmarks for frontier AI alignment. Choose D2l if you want to learn; choose Surge if you need to rigorously evaluate or align cutting-edge models.
D2l Zh vs Praktika
Praktika and D2L Zh serve completely different needs: one is a mobile AI-tutor app for spoken language practice, the other is a free interactive textbook for deep learning. Choose Praktika if you want to improve conversational fluency in a new language via AI tutors; choose D2L Zh if you're a beginner or intermediate learner looking to understand and implement deep learning models with hands-on code. There is no direct competition.
Alternatives to D2l Zh
View allFrequently Asked Questions
Categories
Best-of guides
Topics
Used D2l Zh? Help shape our editorial sentiment research.