Deep Learning
by Ian Goodfellow
Ian Goodfellow's definitive 800-page textbook on deep learning - the comprehensive mathematical foundation for modern AI
"Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction".
Editorial Summary
This is the foundational textbook on deep learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, published by MIT Press in 2016. Written by three of the most innovative researchers in the field, this is the first comprehensive textbook on the subject. The 800-page work covers deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and applications in natural language processing, computer vision, and bioinformatics, while also addressing theoretical topics like autoencoders, representation learning, and deep generative models. Goodfellow, a research scientist at OpenAI and inventor of generative adversarial networks, created what Yann LeCun calls "the AI bible" and Elon Musk describes as "the only comprehensive book on the subject". The book explains how computers can learn from experience through hierarchical concepts, building complicated ideas from simpler ones in deep layers.
Perspective
"Deep Learning is the book that the field built itself around — reading it gives you the mathematical foundations that every paper, every architecture, every subsequent breakthrough assumes you already have. Goodfellow, Bengio, and Courville's distinctive contribution is comprehensiveness: no other single volume covers the full theoretical landscape from feedforward networks through generative models with the same rigor and clarity. Anyone who wants to read primary AI research rather than just its popularizations needs this as their foundation."
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