Transformers for machine learning : a deep dive
Kamath, Uday
Transformers for machine learning : a deep dive by Uday Kamath, Kenneth L Graham and Wael Emara - New york : Chapman and Hall, ©2022 - xxv, 257 p. : ill. ; 23 cm. - Chapman & Hall/CRC machine learning & pattern 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"--
9780367767341
Neural networks (Computer science).
Computational intelligence.
Machine learning.
CB 006.3 / KAM-T
Transformers for machine learning : a deep dive by Uday Kamath, Kenneth L Graham and Wael Emara - New york : Chapman and Hall, ©2022 - xxv, 257 p. : ill. ; 23 cm. - Chapman & Hall/CRC machine learning & pattern 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"--
9780367767341
Neural networks (Computer science).
Computational intelligence.
Machine learning.
CB 006.3 / KAM-T