Deep Learning by Goodfellow Ian (44 items)

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MIT, 2016. Paperback. Like New. 0x0x0. Softcover. Chinese text. Good binding and cover. Minor shelf wear. Clean, unmarked pages.

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An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject."
—Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX
Deep 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.
The text offers mathematical and conceptual background, covering relevant concepts in linear algebra,… Read more

MIT Press, 2016-11-18. hardcover. Used: Good. 9.10x7.20x1.10. Buy with confidence. Excellent Customer Service & Return policy.

The MIT Press. A lovely, tight, clean hardcover in very good condition; faintly bumped upper corners. No dust jacket, as issued. We are a brick-and-mortar store and sell our own inventory. . Very Good. Hardcover. 2016.

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The MIT Press, 2016-11-18. hardcover. Very Good. 9x7x1. a very nice copy! clean and tight pages. fast and secure shipping.

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The MIT Press, November 2016. Hardcover. VG/No Jacket. used hardcover copy, lacking a dust jacket (as issued). light shelfwear, corners perhaps slightly bumped. pages and binding are clean, straight, and tight. there are no marks to the text or other serious flaws.

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ABOUT THE BOOK: Deep 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. The 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… Read more

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