{"product_id":"deep-learning-adaptive-computation-and-machine-learning-series-hardcover-isbn-9780262035613","title":"Deep Learning (Adaptive Computation and Machine Learning series) - Hardcover - ISBN: 9780262035613","description":"\u003cp\u003e**Deep Learning Book – Ian Goodfellow, Yoshua Bengio \u0026amp; Aaron Courville (Official MIT Press Edition) - \u003c\/p\u003e\n\u003cp style=\"box-sizing: border-box; padding: 0px; margin: -4px 0px 14px; color: rgb(15, 17, 17); font-family: 'Amazon Ember', Arial, sans-serif; font-size: 14px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; white-space: normal; background-color: rgb(255, 255, 255); text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;\"\u003e\u003cspan style=\"box-sizing: border-box;\"\u003eDeep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp style=\"box-sizing: border-box; padding: 0px; margin: -4px 0px 14px; color: rgb(15, 17, 17); font-family: 'Amazon Ember', Arial, sans-serif; font-size: 14px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; white-space: normal; background-color: rgb(255, 255, 255); text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;\"\u003e\u003cspan style=\"box-sizing: border-box;\"\u003eThe text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.\u003c\/span\u003e**\u003c\/p\u003e\n\u003cp\u003e**What’s Inside This Bestselling Book:**\u003cbr\u003e• Complete authoritative guide to deep learning – from basics to advanced topics (neural networks, CNNs, RNNs, GANs, reinforcement learning, optimization, regularization \u0026amp; more).\u003cbr\u003e• Written by the pioneers of the field – Ian Goodfellow, Yoshua Bengio \u0026amp; Aaron Courville.\u003cbr\u003e• Clear explanations, mathematical rigor \u0026amp; practical insights – the #1 recommended textbook worldwide for AI\/ML courses.\u003cbr\u003e• Full-color figures, algorithms \u0026amp; real-world examples.\u003c\/p\u003e\n\u003cp\u003e**Why This Book is Essential:**\u003cbr\u003eThe “Deep Learning Bible” used by Stanford, MIT, Google, OpenAI researchers, data scientists, Kaggle competitors \u0026amp; self-learners. Perfect for undergrad\/grad students, professionals switching to AI, or anyone serious about deep learning. Still the gold standard even in 2026.\u003c\/p\u003e\n\u003cp\u003e**Why Buy From Us (Not Other Marketplaces)?**\u003cbr\u003e• Official MIT Press edition – brand new \u0026amp; sealed\u003cbr\u003e• Cheaper than separate purchases\u003cbr\u003e• Fast USA shipping (4–5 working days)\u003cbr\u003e• Secure packaging, no damage\u003cbr\u003e• 30-day money-back guarantee\u003c\/p\u003e\n\u003cp\u003eLimited stock at this special price – master deep learning today!  \u003cbr\u003e**Add to Cart \u0026amp; Become an AI Expert Now!** \u003c\/p\u003e","brand":"TextbookCart","offers":[{"title":"Default Title","offer_id":48627890389235,"sku":null,"price":74.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0805\/3073\/5347\/files\/deeplearning-f.webp?v=1773300942","url":"https:\/\/www.textbookcart.com\/products\/deep-learning-adaptive-computation-and-machine-learning-series-hardcover-isbn-9780262035613","provider":"TextbookCart","version":"1.0","type":"link"}