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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
300			_axxv, 257 p. : _bill. ; _c23 cm.
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.
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
999			_c172364 _d172364