Understanding Deep Learning
FreeMath + intuition + Python notebooks (Simon Prince).
FreeFree tier
About Understanding Deep Learning
Understanding Deep Learning is a free, open-source textbook by Simon J.D. Prince that provides a rigorous yet accessible introduction to deep learning. It combines mathematical foundations with intuitive explanations and practical Python notebooks, covering topics from basic neural networks to modern transformer architectures. The book is designed for self-study or as a university course resource, with all code and figures available online.
Key Features
Free and open-source online textbook
Accompanied by interactive Jupyter notebooks
Mathematical rigor with intuitive explanations
Covers modern architectures including CNNs, RNNs, transformers, and generative models
Includes exercises and self-assessment questions
All figures and code are openly available
Pros & Cons
Pros
- Completely free and open access
- Combines theory with practical code examples
- Comprehensive coverage of modern deep learning topics
- Well-structured with clear explanations
- Regularly updated by the author
Cons
- Requires a solid mathematical background (calculus, linear algebra, probability)
- Not a beginner-friendly introduction to AI; assumes some prior knowledge
- No formal certification or graded assignments
- The notebooks are for illustration, not a full project-based curriculum
Best For
Self-study of deep learning fundamentalsPrimary or supplementary material for university coursesPractical learning with hands-on Python notebooksReference for researchers and practitioners
FAQ
Is Understanding Deep Learning truly free?
Yes, the textbook is freely available online as a PDF, and all accompanying code and figures are open source.
What prerequisites do I need?
A solid understanding of calculus, linear algebra, probability, and basic machine learning is recommended. The book is not for absolute beginners.
Can I use this book for a university course?
Yes, many instructors use it as a primary or supplementary text. The book includes exercises and is structured for semester-long courses.
Does the book cover transformer models?
Yes, the latest edition includes chapters on transformers, large language models, and generative AI.