动手学深度学习

Textbook that emphasizes practical...

4.0| Editor Rating

Editor Review

Hands-On Machine Learning with Deep Learning is a highly practical textbook suitable for readers who want to master deep learning from theory to practice. Its biggest highlight is that each chapter includes runnable code, making the learning process more interactive and intuitive. The content is well-structured, covering multiple topics from basics to advanced concepts, and it has been adopted by universities worldwide, indicating its quality is widely recognized. However, some content may be complex for beginners, requiring a certain level of math and programming knowledge. Overall, the book holds high reference value in the field of deep learning education and is recommended with a four-star rating.

AI Tools Navigator Editorial TeamUpdated: 2026-08-19

What is 动手学深度学习

Hands-On Machine Learning with Deep Learning is a textbook that emphasizes practical implementation to help readers deeply understand theoretical concepts. It is divided into multiple chapters, covering fundamental topics like linear regression and softmax regression, as well as more complex models such as convolutional neural networks (CNN), recurrent neural networks (RNN), Transformer, and attention mechanisms. Each chapter includes runnable Jupyter Notebooks, allowing readers to experiment, adjust parameters, and observe results. The content is used by over 500 universities in more than 70 countries and is available in both Chinese and English, with implementations in PyTorch, TensorFlow, and PaddlePaddle. The book also features multiple real-world case studies, such as Kaggle image classification and housing price prediction competitions, to help readers gain practical experience. The author team includes experts from Amazon, Carnegie Mellon University, and Google, and the content has been refined by over 200 community contributors to ensure its accuracy and professionalism.

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Basic Info

Category:

Best For

DevelopersResearchersStudents

Difficulty: Intermediate

动手学深度学习 Key Features

Runnable code examples
Each section in the book includes runnable Jupyter Notebooks, allowing readers to execute code directly in local or cloud environments. By adjusting parameters and model structures, readers can gain immediate feedback and deepen their understanding of deep learning algorithms.
Support for multiple frameworks
The textbook provides implementations in PyTorch, TensorFlow, PaddlePaddle, and NumPy/MXNet, making it convenient for learners with different technical stacks to choose the framework that suits their needs.
Practical projects and Kaggle competitions
The book includes multiple practical projects, such as image classification and housing price prediction, which are based on real-world datasets. These projects help readers apply theoretical knowledge to real scenarios and enhance their practical skills.
Active community support
Each chapter ends with links to discuss questions with thousands of learners. The community has over 200 contributors, and the content is continuously updated and optimized to ensure its practicality and accuracy.

动手学深度学习 Key Advantages

Comprehensive content covering multiple core topics in deep learning.
Provides runnable code for practical experimentation and debugging.
Adopted by universities worldwide, indicating strong academic recognition.

动手学深度学习 Use Cases

University teaching and course support
The book is used by over 500 universities in more than 70 countries as a textbook or reference for deep learning courses, suitable for teaching in computer science, artificial intelligence, and data science-related disciplines.
Personal deep learning learning
For individuals who want to start learning deep learning from scratch, the book provides clear theoretical explanations and runnable code examples, helping users gradually master the core concepts and practical skills of deep learning.
Framework migration and multilingual learning
The book supports implementations across multiple deep learning frameworks, making it suitable for developers who want to migrate between frameworks. It also provides both Chinese and English versions, facilitating learning for users with different language backgrounds.

Frequently Asked Questions

Is this book suitable for beginners with no prior experience?▼

Hands-On Machine Learning with Deep Learning starts with foundational topics such as linear algebra, calculus, and probability, gradually guiding readers into the field of deep learning. While some content is advanced, the overall structure is clear and suitable for beginners with basic math and programming knowledge. Users with no prior experience may need to supplement their knowledge of fundamentals.

Does the book provide video teaching resources?▼

The official website mentions that teaching videos based on the PyTorch version of the book are available on Bilibili, and videos based on earlier drafts are also available on Bilibili and YouTube. However, these videos are not officially produced but uploaded by third parties or community members, and the content may differ slightly from the current version.

Does the book support multiple languages?▼

Hands-On Machine Learning with Deep Learning is available in both Chinese and English versions, with consistent content across both. The English version is suitable for non-Chinese readers, while the Chinese version is more accessible for Chinese learners, facilitating lookup and comprehension.