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. Transfor...

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Bibliographic Details

Main Authors:	Kamath, Uday (Author), Graham, Kenneth L (Author), Emara, Wael (Author)
Format:	Electronic eBook
Language:	English
Published:	Boca Raton ; London ; New York CRC Press 2022
Edition:	First edition
Series:	Chapman & Hall/CRC machine learning & pattern recognition
Subjects:	Soft Computing Deep Learning Neuronales Netz Maschinelles Lernen Electronic books
Online Access:	DE-863 DE-862 DE-91 DE-29 Volltext
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. Key Features: A comprehensive reference book for detailed explanations for every algorithm and techniques related to the transformers. 60+ transformer architectures covered in a comprehensive manner. A book for understanding how to apply the transformer techniques in speech, text, time series, and computer vision. Practical tips and tricks for each architecture and how to use it in the real world. Hands-on case studies and code snippets for theory and practical real-world analysis using the tools and libraries, all ready to run in Google Colab. 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
Physical Description:	1 Online-Ressource (xxv, 257 Seiten) Illustrationen, Diagramme
ISBN:	9781003170082 9781000587098
DOI:	10.1201/9781003170082