Transformers for machine learning : a deep dive
Material type: TextSeries: Chapman & Hall/CRC machine learning & pattern recognitionPublication details: New york : Chapman and Hall, ©2022Description: xxv, 257 p. : ill. ; 23 cmISBN:- 9780367767341
- CB 006.3 KAM-T
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Books | IIITD Reference | Computer Science and Engineering | CB 006.3 KAM-T (Browse shelf(Opens below)) | Available | DBT Grant project | 012952 | ||
Books | IIITD General Stacks | Computer Science and Engineering | CB 006.3 KAM-T (Browse shelf(Opens below)) | Available | DBT Grant project | 012951 |
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652.8 GAI-C Cryptanalysis : | 658.404 SIN-M Machine learning for finance : | 670.427 KOR-C Computer control of manufacturing systems | CB 006.3 KAM-T Transformers for machine learning : a deep dive | CB 006.31 MAY-D Deep learning on graphs | CB 174.95 FLE-R Responsible data science : transparency and fairness in algorithms | http://shodhganga.inflibnet.ac.in/handle/10603/26898 Emerging covariates of face recognition |
Includes bibliographical references and index.
Deep Learning and Transformers: An Introduction Transformers: Basics and Introduction Bidirectional Encoder Representations from Transformers (BERT) Multilingual Transformer Architectures Transformer Modifications Pre-trained and Application-Specific Transformers Interpretability and Explainability Techniques for Transformers.
"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"--
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