9789811300615 - neural representations of natural language (studies in computational intelligence, 783, band 783) von white, lyndon; togneri, roberto; liu, wei; bennamoun, mohammed (4 Ergebnisse)
Neural Representations of Natural Language (Studies in Computational Intelligence, 783)
White, Lyndon; Togneri, Roberto; Liu, Wei; Bennamoun, Mohammed
Sprache: Englisch
Verlag: Springer, 2018
Serie: Buch 299 von 538 - Studies in Computational Intelligence
- Hardcover
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Sprache: Englisch
Verlag: Springer Singapore, 2018
Serie: Buch 299 von 538 - Studies in Computational Intelligence
- Hardcover
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Neural Representations of Natural Language
White, Lyndon (Author)/ Togneri, Roberto (Author)/ Liu, Wei (Author)/ Bennamoun, Mohammed (Author)
Sprache: Englisch
Verlag: Springer, 2018
Serie: Buch 299 von 538 - Studies in Computational Intelligence
- Hardcover
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Hardcover. Zustand: Brand New. 122 pages. 9.25x6.25x0.50 inches. In Stock.
Sprache: Englisch
Verlag: Springer Nature Singapore, Springer Nature Singapore, 2018
Serie: Buch 299 von 538 - Studies in Computational Intelligence
- Hardcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book offers an introduction to modern natural language processing using machine learning, focusing on how neural networks create a machine interpretable representation of the meaning of natural language. Language is crucially linked to ideas - as Webst…er's 1923 'English Composition and Literature' puts it: 'A sentence is a group of words expressing a complete thought'. Thus the representation of sentences and the words that make them up is vital in advancing artificial intelligence and other 'smart' systems currently being developed. Providing an overview of the research in the area, from Bengio et al.'s seminal work on a 'Neural Probabilistic Language Model' in 2003, to the latest techniques, this book enables readers to gain an understanding of how the techniques are related and what is best for their purposes. As well as a introduction to neural networks in general and recurrent neural networks in particular,this book details the methods used for representing words, senses of words, and larger structures such as sentences or documents.The bookhighlights practical implementations and discusses many aspects that are often overlooked or misunderstood.The book includes thorough instruction on challenging areas such as hierarchicalsoftmax and negative sampling, to ensure the reader fully and easily understands the details of how the algorithms function.Combining practical aspects with a more traditional review of the literature, it is directly applicable to a broad readership.It is an invaluable introduction for early graduate students working in natural language processing; a trustworthy guide for industry developers wishing to make use of recent innovations; and a sturdy bridgefor researchers already familiar with linguistics or machine learning wishing to understand the other.
