Table Of Contents
Table Of Contents

《动手学深度学习》

《动手学深度学习》

面向中文读者的能运行、可讨论的深度学习教科书
冀ICP备19002468号-2

被全球40个国家175所大学用于教学

公告

阿斯顿·张

亚马逊高级科学家,美国伊利诺伊大学香槟分校计算机科学博士。

李沐

亚马逊资深首席科学家,美国卡内基梅隆大学计算机系博士。

扎卡里 C. 立顿

亚马逊科学家,美国卡内基梅隆大学助理教授,加州大学圣迭戈分校计算机科学博士。

亚历山大 J. 斯莫拉

亚马逊副总裁/杰出科学家,德国柏林工业大学计算机科学博士。

以及来自社区的 100+ 位贡献者

为本书贡献

Star Fork

学术界推荐

“Dive into this book if you want to dive into deep learning!”

韩家炜

ACM 院士、IEEE 院士
美国伊利诺伊大学香槟分校计算机系 Michael Aiken Chair 教授

“This is a highly welcome addition to the machine learning literature.”

Bernhard Schölkopf

ACM 院士、德国国家科学院院士
德国马克斯•普朗克研究所智能系统院院长

“书中代码可谓‘所学即所用’。”

周志华

ACM 院士、IEEE 院士、AAAS 院士
南京大学计算机科学与技术系主任

“这本书可以帮助深度学习实践者快速提升自己的能力。”

张潼

ASA 院士、IMS 院士
香港科技大学计算机系和数学系教授

工业界推荐

“一本优秀的深度学习教材,值得任何想了解深度学习何以引爆人工智能革命的人关注。”

黄仁勋

NVIDIA创始人 & CEO

“《动手学深度学习》是最适合工业界研发工程师学习的。我毫无保留地向广大的读者们强烈推荐。”

余凯

地平线公司创始人 & CEO

“强烈推荐这本书!我特别赞赏这种手脑一体的学习方式。”

漆远

蚂蚁金服副总裁、首席AI科学家

“《动手学深度学习》是一本很容易让学习者上瘾的书。”

沈强

将门创投创始合伙人

每一小节都是可以运行的 Jupyter 记事本

你可以自由修改代码和超参数来获取及时反馈,从而积累深度学习的实战经验。

公式 + 图示 + 代码

我们不仅结合文字、公式和图示来阐明深度学习里常用的模型和算法,还提供代码来演示如何从零开始实现它们,并使用真实数据来提供一个交互式的学习体验。

活跃社区支持

你可以通过每个章节最后的链接来同社区的数千名小伙伴一起讨论学习。

本书(中英文版)被用作教材或参考书

Alexandria University
Amity University
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Australian National University
Birla Institute of Technology and Science, Hyderabad
Brown University
Cairo University
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IMT Mines Alès
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Indian Institute of Technology Mandi
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Indira Gandhi National Open University
Indraprastha Institute of Information Technology, Delhi
Institut catholique d'arts et métiers (ICAM)
Institut de recherche en informatique de Toulouse
Institut Supérieur d'Informatique et des Techniques de Communication
Institut Supérieur De L'electronique Et Du Numérique
Instituto Tecnológico Autónomo de México
İstanbul Teknik Üniversitesi
IT-Universitetet i København
King Abdullah University of Science and Technology
Kongu Engineering College
KPR Institute of Engineering and Technology
Kyungpook National University
Lancaster University
Leibniz Universität Hannover
Leuphana University of Lüneburg
London School of Economics & Political Science
Make School
Massachusetts Institute of Technology
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Minia University
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National Institute of Technical Teachers Training&Research
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Shenzhen University
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St. Pölten University of Applied Sciences
Stanford University
Stevens Institute of Technology
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Technische Universiteit Delft
Tekirdağ Namık Kemal Üniversitesi
Texas A&M University
Thapar Institute of Engineering and Technology
The State University of New York at Binghamton
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Tsinghua University
Universidad Carlos III de Madrid
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Universidad Nacional Agraria La Molina
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英文版引用


      @book{zhang2020dive,
          title={Dive into Deep Learning},
          author={Aston Zhang and Zachary C. Lipton and Mu Li and Alexander J. Smola},
          note={\url{http://www.d2l.ai}},
          year={2020}
      }
	  

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