动手学深度学习
Dive into Deep Learning is a textbook and course that combines theory with hands-on practice, covering everything from linear neural networks to attention mechanisms, with code examples, datasets, videos and a forum. It suits both classroom teaching and self-study.
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Suitable for students and engineers who want a systematic, hands-on foundation in deep learning; not suitable for readers who only want to call LLM APIs without learning the underlying mechanics.
Verified facts
What is 动手学深度学习
Dive into Deep Learning is a textbook and course that combines theory with hands-on practice, covering everything from linear neural networks to attention mechanisms, with code examples, datasets, videos and a forum. It suits both classroom teaching and self-study.
Key features of 动手学深度学习
- University teachers adopting it as the textbook for a deep learning course
- Engineers self-studying neural network fundamentals through code experiments
- Research beginners building a complete knowledge framework chapter by chapter
- Practitioners consulting relevant chapters for computer vision or NLP projects
Good for
- Complete theory path from prerequisites to modern architectures
- Strong hands-on orientation with code examples, datasets and experiments
- Rich supporting resources including online docs, teaching videos and a forum
Watch out
- Requires basic math and programming skills to follow
- The full book is long and demands months of steady effort
- Cutting-edge LLM application topics may not be updated as fast as the community moves
How to use 动手学深度学习
- Visit the Chinese site of Dive into Deep Learning
- Start with the introduction or prerequisites chapters based on your background
- Set up the runtime environment and run the code examples along with the text
- Finish each chapter's experiments and tweak parameters to observe results
- Ask questions in the companion forum to consolidate difficult points
Who 动手学深度学习 is for
Difficulty: Beginner
- University teachers adopting it as the textbook for a deep learning course
- Engineers self-studying neural network fundamentals through code experiments
- Research beginners building a complete knowledge framework chapter by chapter
- Practitioners consulting relevant chapters for computer vision or NLP projects
FAQ
Is Dive into Deep Learning suitable for absolute beginners?
The book assumes some math and programming background. A dedicated prerequisites chapter covers the needed foundations, so it is best to work through that part first.
Can it be used as a university textbook?
Yes. The official description notes its clear structure makes it suitable both as a course textbook and for self-study.
What topics does the book cover?
It covers linear neural networks, multilayer perceptrons, convolutional networks, recurrent networks, attention, optimization algorithms, computational performance, computer vision and natural language processing.
Sources and verification
Sources: zh.d2l.ai (opens in a new tab)
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