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Deep learning for natural language processing : a gentle introduction / Mihai Surdeanu, Marco Antonio Valenzuela-Esc©Łrcega

By: Contributor(s): Material type: TextTextPublisher: Cambridge : Cambridge University Press, 2024Description: 1 online resource (xviii, 325 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781009026222 (ebook)
Subject(s): Additional physical formats: Print version: : No titleDDC classification:
  • 006.35 23
LOC classification:
  • QA76.9.N38 S87 2024
Online resources: Summary: Deep Learning is becoming increasingly important in a technology-dominated world. However, the building of computational models that accurately represent linguistic structures is complex, as it involves an in-depth knowledge of neural networks, and the understanding of advanced mathematical concepts such as calculus and statistics. This book makes these complexities accessible to those from a humanities and social sciences background, by providing a clear introduction to deep learning for natural language processing. It covers both theoretical and practical aspects, and assumes minimal knowledge of machine learning, explaining the theory behind natural language in an easy-to-read way. It includes pseudo code for the simpler algorithms discussed, and actual Python code for the more complicated architectures, using modern deep learning libraries such as PyTorch and Hugging Face. Providing the necessary theoretical foundation and practical tools, this book will enable readers to immediately begin building real-world, practical natural language processing systems
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books Books COMSATS University Abbottabad Campus 006.31 SUR (Browse shelf(Opens below)) Available 10003000038732
Total holds: 0

Title from publisher's bibliographic system (viewed on 02 Feb 2024)

Available to OhioLINK libraries

Deep Learning is becoming increasingly important in a technology-dominated world. However, the building of computational models that accurately represent linguistic structures is complex, as it involves an in-depth knowledge of neural networks, and the understanding of advanced mathematical concepts such as calculus and statistics. This book makes these complexities accessible to those from a humanities and social sciences background, by providing a clear introduction to deep learning for natural language processing. It covers both theoretical and practical aspects, and assumes minimal knowledge of machine learning, explaining the theory behind natural language in an easy-to-read way. It includes pseudo code for the simpler algorithms discussed, and actual Python code for the more complicated architectures, using modern deep learning libraries such as PyTorch and Hugging Face. Providing the necessary theoretical foundation and practical tools, this book will enable readers to immediately begin building real-world, practical natural language processing systems

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