| 000 | 02290nam\a2200277\a\4500 | ||
|---|---|---|---|
| 001 | 57709 | ||
| 005 | 20260222114616.0 | ||
| 008 | 260222s2022 nyu ob 001 0 eng | ||
| 020 |
_a9781316519332 _q(epub) |
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| 020 |
_z9781316519332 _q(hardback) |
||
| 040 |
_aDLC _beng _cDLC _erda _dBD-DhIUB |
||
| 082 | 0 | 0 |
_a006.31 _223/eng20220215 _bR639p |
| 100 | 1 |
_aRoberts, Daniel A., _d1987- _eauthor. |
|
| 245 | 1 | 4 |
_aThe principles of deep learning theory : _ban effective theory approach to understanding neural networks / _cDaniel A. Roberts and Sho Yaida based on research in collaboration with Boris Hanin. |
| 260 |
_aUnited Kingdom _bCambridge University Press, _c2022 |
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| 300 |
_aix,460p;. _c24cm |
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| 504 | _aIncludes bibliographical references and index. | ||
| 520 | _a"This textbook establishes a theoretical framework for understanding deep learning models of practical relevance. With an approach that borrows from theoretical physics, Roberts and Yaida provide clear and pedagogical explanations of how realistic deep neural networks actually work. To make results from the theoretical forefront accessible, the authors eschew the subject's traditional emphasis on intimidating formality without sacrificing accuracy. Straightforward and approachable, this volume balances detailed first-principle derivations of novel results with insight and intuition for theorists and practitioners alike. This self-contained textbook is ideal for students and researchers interested in artificial intelligence with minimal prerequisites of linear algebra, calculus, and informal probability theory, and it can easily fill a semester-long course on deep learning theory. For the first time, the exciting practical advances in modern artificial intelligence capabilities can be matched with a set of effective principles, providing a timeless blueprint for theoretical research in deep learning"-- | ||
| 526 |
_aCSE _bps _lREF |
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| 541 | _aTRIM | ||
| 650 | 0 | _aDeep learning (Machine learning) | |
| 650 | 7 |
_aSCIENCE / Physics / Mathematical & Computational _2bisacsh |
|
| 776 | 0 | 8 |
_iPrint version: _aRoberts, Daniel A., 1987- _tPrinciples of deep learning theory _dNew York : Cambridge University Press, 2022 _z9781316519332 _w(DLC) 2021060635 |
| 942 |
_2ddc _cBK |
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| 999 |
_c57709 _d57883 |
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