000 | 02058nam a22002777a 4500 | ||
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003 | IIITD | ||
005 | 20240808020004.0 | ||
008 | 240508b |||||||| |||| 00| 0 eng d | ||
020 | _a9780367767341 | ||
040 | _aIIITD | ||
082 | 0 | 0 |
_aCB 006.3 _bKAM-T |
100 | 1 | _aKamath, Uday | |
245 | 1 | 0 |
_aTransformers for machine learning : _ba deep dive _cby Uday Kamath, Kenneth L Graham and Wael Emara |
260 |
_aNew york : _bChapman and Hall, _c©2022 |
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300 |
_axxv, 257 p. : _bill. ; _c23 cm. |
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440 | _aChapman & Hall/CRC machine learning & pattern recognition | ||
504 | _aIncludes bibliographical references and index. | ||
505 |
_tDeep Learning and Transformers: An Introduction _tTransformers: Basics and Introduction _tBidirectional Encoder Representations from Transformers (BERT) _tMultilingual Transformer Architectures _tTransformer Modifications _tPre-trained and Application-Specific Transformers _tInterpretability and Explainability Techniques for Transformers. |
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520 | _a"Transformers are becoming a core part of many neural network architectures, employed in a wide range of applications such as NLP, Speech Recognition, Time Series, and Computer Vision. Transformers have gone through many adaptations and alterations, resulting in newer techniques and methods. Transformers for Machine Learning: A Deep Dive is the first comprehensive book on transformers. The theoretical explanations of the state-of-the-art transformer architectures will appeal to postgraduate students and researchers (academic and industry) as it will provide a single entry point with deep discussions of a quickly moving field. The practical hands-on case studies and code will appeal to undergraduate students, practitioners, and professionals as it allows for quick experimentation and lowers the barrier to entry into the field"-- | ||
650 | 0 | _aNeural networks (Computer science). | |
650 | 0 | _aComputational intelligence. | |
650 | 0 | _aMachine learning. | |
700 | _aGraham, Kenneth L | ||
700 | _aEmara, Wael | ||
942 |
_2ddc _cBK _02 |
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999 |
_c172364 _d172364 |