Deep Learning

Deep Learning is the complete textbook by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, published by MIT Press. The full text is available to read online at no charge — the authors state it will remain available for free. It covers math basics, machine learning fundamentals, modern neural networks, and research topics. The book is written for students and practitioners entering the field. A printed edition can also be ordered. Each chapter builds on the last, from linear algebra to generative models. Check the official site for updates and announcements.

Key Features

1. Free Online Reading

The entire book is complete and free to read on the official website, chapter by chapter.

2. Three-Part Structure

Part one covers applied math and machine learning basics, part two covers modern deep networks, and part three covers research.

3. Chapter Exercises

The site hosts exercises for the Linear Algebra chapter, with more contributed by readers, so you can practice the math.

4. Lecture Slides and Videos

The site offers chapter slides plus recorded video lectures, so visual learners can follow the book by video.

5. LaTeX Templates

Downloadable LaTeX style files let readers reuse the book's exact math notation in their own documents.

6. Errata and Citations

The site lists corrections to printed editions and gives a ready-made BibTeX entry for citing the book.

FAQ

Who wrote the Deep Learning book?

Ian Goodfellow, Yoshua Bengio, and Aaron Courville wrote it. MIT Press published it in 2016, and all three authors work in machine learning research.

How can I read the full text without buying the print edition?

Read every chapter on the official site, deeplearningbook.org. The authors keep the online version free. MIT Press also sells a print copy.

What topics does the book cover?

It starts with linear algebra, probability, and machine learning basics, then moves to deep networks like convolutional and recurrent nets, and ends with research topics like autoencoders.

What math should I know before reading it?

The book teaches applied math in its first chapters, but comfort with basic calculus and linear algebra helps. Those early chapters are designed to get readers up to speed.

Is there a way to cite the book?

Yes. The official site provides a BibTeX entry you can copy into academic papers and LaTeX documents.

Conclusion

The Deep Learning book suits students and engineers who want a serious, math-first path into neural networks. It is free to read online, thorough, and written by top researchers. Beginners should plan to work through the math chapters slowly. Pair it with the linked lectures for video explanations, and check the official site for errata and updates.