Description
This comprehensive textbook delves into the intricacies of deep learning architecture, providing readers with a robust understanding of its application in natural language processing and speech recognition. The authors, Uday Kamath, John Liu, and James Whitaker, expertly break down complex concepts, making them accessible to readers at different proficiency levels.
Through a series of well-structured chapters, the book explores various NLP tasks, offering practical examples and real-world applications. It emphasizes the significance of deep learning methods in enhancing the performance of language models and speech systems, making it an invaluable resource for researchers and practitioners alike.
By bridging theoretical foundations with practical implementation, the authors equip readers with the knowledge necessary to navigate the rapidly evolving fields of NLP and speech recognition. This textbook stands as a key reference for anyone looking to deepen their understanding of deep learning's transformative role in these domains.
Through a series of well-structured chapters, the book explores various NLP tasks, offering practical examples and real-world applications. It emphasizes the significance of deep learning methods in enhancing the performance of language models and speech systems, making it an invaluable resource for researchers and practitioners alike.
By bridging theoretical foundations with practical implementation, the authors equip readers with the knowledge necessary to navigate the rapidly evolving fields of NLP and speech recognition. This textbook stands as a key reference for anyone looking to deepen their understanding of deep learning's transformative role in these domains.
Book Details
Format
Kindle
Pages
621 pages
Language
English
Published
Jun 10, 2019
Publisher
Springer
ISBN-10
3030145964
ISBN-13
9783030145965