Transformers for machine learning : a deep dive

By:

Kamath, Uday

Contributor(s):

Material type: Text

9780367767341

Subject(s):

DDC classification:

CB 006.3 KAM-T

Contents:

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.

Summary: "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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Holdings
Item type	Current library	Collection	Call number	Status	Notes	Date due	Barcode	Item holds
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

Total holds: 0

Includes bibliographical references and index.

"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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