Deep Learning by Ian Goodfellow, Yoshua Bengio & Aaron Courville (MIT Press, Hardcover)

Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville is the MIT Press hardcover textbook in the Adaptive Computation and Machine Learning series, written for graduate students and practitioners who need the mathematical foundations of neural networks.

Deep learning lets computers learn from experience and understand the world through a hierarchy of concepts, each built from simpler ones. This book was written by three of the researchers who shaped the field and is the text most university AI courses still assign for theory. It opens with the applied math you need, moves through the practical techniques used in industry, and closes with research-level topics.

What's included

• Deep Learning, first edition hardcover (MIT Press, 2016)
• Part I: linear algebra, probability and information theory, numerical computation, and machine learning basics
• Part II: deep feedforward networks, regularization, optimization, convolutional networks, sequence modeling, and practical methodology
• Part III: linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, approximate inference, and deep generative models
• Surveys of applications in natural language processing, speech recognition, computer vision, and recommendation systems
• ISBN-13: 9780262035613

Who it's for

Graduate and advanced undergraduate students in computer science, statistics, or engineering; data scientists and machine learning engineers who want the reasoning behind the tools they use; and researchers who need a single reference for the core theory. Readers should be comfortable with calculus, linear algebra, and basic probability.

Why this edition

This is the printed MIT Press edition of a book many people have only read on a screen. The hardcover holds up to repeated use, the equations and figures are easier to study on paper, and it sits on the shelf next to CLRS and Bishop in most research labs. The content is the single edition the authors published, so there is no newer printed version to wait for.

Frequently asked questions

Is the Deep Learning book by Goodfellow still relevant?

Yes for fundamentals. Transformers and diffusion models came later, but the math, optimization, regularization, and CNN and RNN chapters remain the base that newer work builds on.

Is there a free version of this book?

The authors host an online HTML version. This listing is the physical MIT Press hardcover for readers who want a printed copy.

What background do I need to read it?

Undergraduate calculus, linear algebra, and probability. Part I reviews these topics, but it is a refresher rather than a first course.

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