| 000 | 02787cam a2200241 i 4500 | ||
|---|---|---|---|
| 005 | 20221206110233.0 | ||
| 008 | 160613t20162016maua b 001 0 eng | ||
| 010 | _a 2016022992 | ||
| 020 | _a9780262035613 (hardcover : alk. paper) | ||
| 020 | _a0262035618 (hardcover : alk. paper) | ||
| 040 | _aS.X.U.K | ||
| 041 | _aEnglish | ||
| 082 | 0 | 0 | _aR 006.31 GOO(DEE) |
| 100 | 1 | _aGoodfellow, Ian, | |
| 245 | 1 | 0 |
_aDeep learning / _cIan Goodfellow, Yoshua Bengio, and Aaron Courville. |
| 260 |
_aMassachusetts _bCambridge _cc2016 |
||
| 300 |
_axxii, 775 pages : _billustrations (some color) ;H.B. _c24 cm. |
||
| 500 | _aAn 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, 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. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors. | ||
| 650 | 0 | _aMachine learning, | |
| 700 | 1 | _aBengio, Yoshua, | |
| 700 | 1 | _aCourville, Aaron, | |
| 942 | _cUCS | ||
| 999 |
_c8120 _d8120 |
||