{"product_id":"deep-learning-goodfellow-bengio-courville-mit-press-hardcover","title":"Deep Learning by Ian Goodfellow, Yoshua Bengio \u0026 Aaron Courville (MIT Press, Hardcover)","description":"\u003cp\u003e\u003cstrong\u003eDeep 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.\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eDeep 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.\u003c\/p\u003e\n\u003ch2\u003eWhat's included\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDeep Learning, first edition hardcover (MIT Press, 2016)\u003c\/li\u003e\n\u003cli\u003ePart I: linear algebra, probability and information theory, numerical computation, and machine learning basics\u003c\/li\u003e\n\u003cli\u003ePart II: deep feedforward networks, regularization, optimization, convolutional networks, sequence modeling, and practical methodology\u003c\/li\u003e\n\u003cli\u003ePart III: linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, approximate inference, and deep generative models\u003c\/li\u003e\n\u003cli\u003eSurveys of applications in natural language processing, speech recognition, computer vision, and recommendation systems\u003c\/li\u003e\n\u003cli\u003eISBN-13: 9780262035613\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eWho it's for\u003c\/h2\u003e\n\u003cp\u003eGraduate 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.\u003c\/p\u003e\n\u003ch2\u003eWhy this edition\u003c\/h2\u003e\n\u003cp\u003eThis 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.\u003c\/p\u003e\n\u003ch2\u003eFrequently asked questions\u003c\/h2\u003e\n\u003ch3\u003eIs the Deep Learning book by Goodfellow still relevant?\u003c\/h3\u003e\n\u003cp\u003eYes 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.\u003c\/p\u003e\n\u003ch3\u003eIs there a free version of this book?\u003c\/h3\u003e\n\u003cp\u003eThe authors host an online HTML version. This listing is the physical MIT Press hardcover for readers who want a printed copy.\u003c\/p\u003e\n\u003ch3\u003eWhat background do I need to read it?\u003c\/h3\u003e\n\u003cp\u003eUndergraduate calculus, linear algebra, and probability. Part I reviews these topics, but it is a refresher rather than a first course.\u003c\/p\u003e\n\u003ch2\u003eDetails\u003c\/h2\u003e\n\u003ctable\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003cth\u003eTitle\u003c\/th\u003e\n\u003ctd\u003eDeep Learning (Adaptive Computation and Machine Learning series)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth\u003eEdition\u003c\/th\u003e\n\u003ctd\u003eFirst Edition (2016)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth\u003eFormat\u003c\/th\u003e\n\u003ctd\u003eHardcover\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth\u003ePublisher\u003c\/th\u003e\n\u003ctd\u003eMIT Press\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth\u003eISBN-13\u003c\/th\u003e\n\u003ctd\u003e9780262035613\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth\u003eCondition\u003c\/th\u003e\n\u003ctd\u003eNew\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e","brand":"MIT Press","offers":[{"title":"Default Title","offer_id":67971825860917,"sku":null,"price":43.78,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1006\/7410\/9749\/files\/deeplearning-f.webp?v=1793133734","url":"https:\/\/nfpabooks.com\/products\/deep-learning-goodfellow-bengio-courville-mit-press-hardcover","provider":"NFPABooks.com","version":"1.0","type":"link"}