Deep learning powers everything from ChatGPT to self-driving cars, medical image diagnosis to real-time language translation. It’s the driving force behind the current AI wave, and understanding how neural networks actually work — not just how to call APIs — separates practitioners from users.
The three books below cover deep learning from three angles: a concise technical primer, a comprehensive mathematical foundation, and a hands-on introduction to neural networks. Together they give you the theory and the intuition needed to work with modern deep learning systems.
Best Free Deep Learning Books
Three open-licensed books that cover deep learning from fundamentals to advanced concepts. All free to download with no registration required.
1. The Little Book of Deep Learning
Author: François Fleuret
License: CC BY-NC-SA 4.0
This is the shortest book on the list and also the most focused. François Fleuret — a professor at the University of Geneva — wrote it as a quick yet rigorous overview of deep learning. It covers supervised learning, convolutional networks, transformers, diffusion models, and reinforcement learning in under 150 pages.
The book assumes a STEM background. It won’t teach you Python or TensorFlow, but it will give you a clear mental model of how each architecture works and why it matters. Perfect if you want to understand the landscape without reading a 700-page textbook.
Read or download “The Little Book of Deep Learning”
2. Understanding Deep Learning
Author: Simon J.D. Prince
License: CC BY-NC-ND
Simon Prince wrote this as the textbook he wished he had when learning deep learning. It starts with the basics of supervised learning and builds up to attention mechanisms, generative models, normalizing flows, and diffusion. Every concept is explained with mathematics first, code second.
This is the book that already has 150+ monthly impressions on Google for the query “understanding deep learning” — people are actively searching for it. The PDF is comprehensive and regularly updated. It’s best suited for readers comfortable with linear algebra and probability who want a thorough treatment.
Read or download “Understanding Deep Learning”
3. A Brief Introduction to Neural Networks
Author: David Kriesel
License: CC BY-ND 3.0
David Kriesel’s book started as seminar notes at the University of Bonn and grew into one of the most widely circulated introductions to neural networks in German and English. It covers the biological inspiration, perceptrons, backpropagation, Hopfield networks, and self-organizing maps.
What makes this book different is the clarity of the explanations. Kriesel has a knack for making complex topics visual and intuitive. It’s older than the other two books and focuses on classic neural network theory rather than modern deep learning, but that theory is the foundation everything else builds on.