{"product_id":"deep-learning-ian-goodfellow","title":"Deep Learning (Adaptive Computation and Machine Learning series) (0262035618)","description":"\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eBuy Deep Learning by Ian Goodfellow Online\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eDeep Learning\u003c\/strong\u003e\u003cspan\u003e (Adaptive Computation and \u003c\/span\u003e\u003cstrong\u003eMachine Learning\u003c\/strong\u003e\u003cspan\u003e series) \u003cstrong\u003e(\u003c\/strong\u003e\u003c\/span\u003e\u003cstrong\u003eISBN-13 9780262035613\u003c\/strong\u003e\u003cspan\u003e\u003cstrong\u003e)\u003c\/strong\u003e by \u003c\/span\u003e\u003cstrong\u003eIan Goodfellow\u003c\/strong\u003e\u003cspan\u003e, \u003c\/span\u003e\u003cstrong\u003eYoshua Bengio\u003c\/strong\u003e\u003cspan\u003e, and \u003c\/span\u003e\u003cstrong\u003eAaron Courville\u003c\/strong\u003e\u003cspan\u003e is the definitive \u003c\/span\u003e\u003cstrong\u003edeep learning textbook\u003c\/strong\u003e\u003cspan\u003e in academic and research settings worldwide. Published by \u003c\/span\u003e\u003cspan\u003eMIT Press\u003c\/span\u003e\u003cspan\u003e, it is the book that university courses, research labs, and machine learning teams assign when they want a single resource that covers both the theory and the practice of modern deep learning with real rigor.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eIf you are serious about understanding how \u003c\/span\u003e\u003cspan\u003edeep learning\u003c\/span\u003e\u003cspan\u003e actually works rather than just applying it as a black box, this is the book you need on your desk. At 45% off the original retail price, it is also the best time to add the \u003c\/span\u003e\u003cstrong\u003eDeep Learning Ian Goodfellow\u003c\/strong\u003e\u003cspan\u003e text to your technical library.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eAbout This Deep Learning Textbook\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eThe \u003c\/span\u003e\u003cspan\u003eDeep Learning Goodfellow\u003c\/span\u003e\u003cspan\u003e book is different from practitioner guides like Keras or PyTorch tutorials. It builds understanding from the mathematical ground up, starting with the linear algebra, probability theory, and numerical computation that underpin every deep learning system, and then moving through the core models, optimization methods, and modern architectures that researchers and engineers actually work with.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eAs the \u003c\/span\u003e\u003cspan\u003eMIT Deep Learning Book\u003c\/span\u003e\u003cspan\u003e in the Adaptive Computation and \u003c\/span\u003e\u003cspan\u003eMachine Learning\u003c\/span\u003e\u003cspan\u003e series, it carries the kind of academic authority that comes from authors who are themselves key contributors to the field. \u003c\/span\u003e\u003cspan\u003eIan Goodfellow\u003c\/span\u003e\u003cspan\u003e invented generative adversarial networks. \u003c\/span\u003e\u003cspan\u003eYoshua Bengio\u003c\/span\u003e\u003cspan\u003e and \u003c\/span\u003e\u003cspan\u003eAaron Courville\u003c\/span\u003e\u003cspan\u003e are Turing Award-level researchers whose work sits at the foundation of modern AI. When you read their explanations of how \u003c\/span\u003e\u003cspan\u003eneural networks\u003c\/span\u003e\u003cspan\u003e learn, you are reading from the people who developed many of those ideas.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eWhat You Will Learn from Deep Learning by Ian Goodfellow\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eMathematical foundations for \u003c\/span\u003e\u003cspan\u003edeep learning\u003c\/span\u003e\u003cspan\u003e: linear algebra, probability, information theory, and numerical methods\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eHow \u003c\/span\u003e\u003cspan\u003emachine learning\u003c\/span\u003e\u003cspan\u003e algorithms learn from data: capacity, overfitting, underfitting, and generalization\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eDeep feedforward networks: how \u003c\/span\u003e\u003cspan\u003eneural networks\u003c\/span\u003e\u003cspan\u003e are structured and trained\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eRegularization techniques: dropout, batch normalization, data augmentation, and more\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eOptimization for \u003c\/span\u003e\u003cspan\u003edeep learning\u003c\/span\u003e\u003cspan\u003e: SGD, momentum, adaptive learning rates, and second-order methods\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eConvolutional \u003c\/span\u003e\u003cspan\u003eneural networks\u003c\/span\u003e\u003cspan\u003e: architecture, pooling, and applications in computer vision\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eRecurrent \u003c\/span\u003e\u003cspan\u003eneural networks\u003c\/span\u003e\u003cspan\u003e and LSTMs: sequence modeling and temporal dependencies\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003ePractical methodology: how to choose hyperparameters, debug models, and design experiments\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eDeep generative models: restricted Boltzmann machines, autoencoders, and generative adversarial networks\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eFrontier research areas: representation learning, structured probabilistic models, and Monte Carlo methods\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eKey Features of Deep Learning (Adaptive Computation and Machine Learning)\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eThe most authoritative \u003c\/span\u003e\u003cspan\u003eDeep Learning Textbook\u003c\/span\u003e\u003cspan\u003e available, used in graduate courses worldwide\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eWritten by \u003c\/span\u003e\u003cspan\u003eIan Goodfellow\u003c\/span\u003e\u003cspan\u003e, \u003c\/span\u003e\u003cspan\u003eYoshua Bengio\u003c\/span\u003e\u003cspan\u003e, and \u003c\/span\u003e\u003cspan\u003eAaron Courville\u003c\/span\u003e\u003cspan\u003e, three of the field's most cited researchers\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003ePart of the \u003c\/span\u003e\u003cspan\u003eMIT Press\u003c\/span\u003e\u003cspan\u003e \u003c\/span\u003e\u003cspan\u003eDeep Learning Adaptive Computation\u003c\/span\u003e\u003cspan\u003e and \u003c\/span\u003e\u003cspan\u003eMachine Learning\u003c\/span\u003e\u003cspan\u003e series\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003e800 pages covering both mathematical foundations and practical \u003c\/span\u003e\u003cspan\u003edeep learning\u003c\/span\u003e\u003cspan\u003e techniques\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eHardcover edition built for lasting reference use\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eCovers the full pipeline from data and optimization through advanced generative models\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eISBN-13 9780262035613\u003c\/span\u003e\u003cspan\u003e, authentic \u003c\/span\u003e\u003cspan\u003eMIT Press\u003c\/span\u003e\u003cspan\u003e hardcover, currently 45% off retail\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eFree standard US shipping on every order\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eWho Should Read This Deep Learning Ian Goodfellow Book?\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eGraduate students in \u003c\/span\u003e\u003cspan\u003emachine learning\u003c\/span\u003e\u003cspan\u003e, computer science, and AI programs use this \u003c\/span\u003e\u003cspan\u003edeep learning textbook\u003c\/span\u003e\u003cspan\u003e as a core course reference. If your program covers \u003c\/span\u003e\u003cspan\u003eneural networks\u003c\/span\u003e\u003cspan\u003e at a theoretical level, this is almost certainly either required or recommended reading.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eResearchers entering the field use it to build the foundational understanding they need to read papers and contribute original work. Software engineers and data scientists who want to go beyond API calls and understand what their \u003c\/span\u003e\u003cspan\u003edeep learning\u003c\/span\u003e\u003cspan\u003e models are actually doing turn to this book when frameworks and tutorials stop being sufficient.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eLecturers and instructors teaching \u003c\/span\u003e\u003cspan\u003emachine learning\u003c\/span\u003e\u003cspan\u003e or \u003c\/span\u003e\u003cspan\u003edeep learning\u003c\/span\u003e\u003cspan\u003e courses keep it as a primary text because the coverage is broad enough for a full semester and rigorous enough to hold up to serious students. Anyone who finds themselves saying 'I want to actually understand this, not just use it' will find this book is the right starting point for that kind of understanding.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eWhy Choose Deep Learning Goodfellow Over Other AI Books?\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eThere are many \u003c\/span\u003e\u003cspan\u003edeep learning\u003c\/span\u003e\u003cspan\u003e books, but most of them fall into one of two camps: they either teach you how to use a framework, or they skim the theory so lightly that you finish the book without really understanding why anything works. The \u003c\/span\u003e\u003cspan\u003eDeep Learning Goodfellow\u003c\/span\u003e\u003cspan\u003e text refuses to make that tradeoff. It covers the mathematics, the intuition, and the practical application, which is why it has remained the standard academic reference in the field since its publication in 2016.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eIt also ages better than framework-specific books. The mathematics of optimization, the principles behind regularization, the theory of convolutional networks, the intuition behind generative models: none of that has gone stale, because it is describing ideas rather than APIs. Engineers who read this book in 2016 still find it useful today, and engineers buying it now will find the same thing in five years.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eWhy Buy from Wisdom Hatch Inc.?\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eWisdom Hatch Inc. is a US-based professional and technical online bookstore that carries authentic, current editions of the academic and technical references that students, researchers, and professionals actually use. Every copy of the \u003c\/span\u003e\u003cspan\u003eDeep Learning Ian Goodfellow\u003c\/span\u003e\u003cspan\u003e hardcover we sell is a genuine \u003c\/span\u003e\u003cspan\u003eMIT Press\u003c\/span\u003e\u003cspan\u003e edition, brand new, not a scan, grey-market import, or used copy.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eAll orders come with secure checkout, free standard shipping to all US states, territories, and APO and FPO addresses, and responsive support at wisdomhatchinc@gmail.com. The current price represents a 45% discount from retail. Add the \u003c\/span\u003e\u003cspan\u003eMIT Deep Learning Book\u003c\/span\u003e\u003cspan\u003e to your order today and pay the lowest price with confidence.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eFrequently Asked Questions\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eQ: What is Deep Learning by Ian Goodfellow about?\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eA: It is a comprehensive deep learning textbook that covers both the mathematical foundations and practical techniques behind modern neural networks and AI. Written by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, it is the most widely referenced academic resource in the field.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eQ: What is the ISBN-13 for Deep Learning by Ian Goodfellow?\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eA: The ISBN-13 is 9780262035613 (ISBN-10: 0262035618). It is published by MIT Press.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eQ: Is this the MIT Deep Learning Book?\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eA: Yes. This is the deep learning book published by The MIT Press as part of the Adaptive Computation and Machine Learning series. It is commonly called the MIT deep learning book in academic and research communities.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eQ: Is this hardcover or paperback?\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eA: This edition is a hardcover. It is 800 pages and published by MIT Press.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eQ: Is Deep Learning by Goodfellow suitable for beginners?\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eA: It is best suited for readers with a solid background in mathematics, including linear algebra, probability, and calculus, as well as some familiarity with programming. It is not an introductory text, but the early chapters do cover mathematical prerequisites for readers who need a refresher.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eQ: Is this the latest edition?\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eA: This is the original 2016 MIT Press edition of Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. It remains the standard academic reference in the field.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e","brand":"Wisdom Hatch Inc.","offers":[{"title":"Default Title","offer_id":50659278618863,"sku":null,"price":54.65,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0836\/3697\/7903\/files\/DeepLearning.jpg?v=1788333739","url":"https:\/\/www.wisdomhatchinc.com\/products\/deep-learning-ian-goodfellow","provider":"Online Bookstore | Professional \u0026 Technical Reference Books | Wisdomhatch","version":"1.0","type":"link"}