Sprache: Englisch
Verlag: Cambridge University Press, 2021
ISBN 10: 1108420214 ISBN 13: 9781108420211
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Sprache: Englisch
Verlag: Cambridge University Press, 2021
ISBN 10: 1108420214 ISBN 13: 9781108420211
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Sprache: Englisch
Verlag: Cambridge University Press, 2021
ISBN 10: 1108420214 ISBN 13: 9781108420211
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In den WarenkorbHardcover. Zustand: Brand New. 470 pages. 9.75x7.25x1.05 inches. In Stock.
Sprache: Englisch
Verlag: Cambridge University Press, 2021
ISBN 10: 1108420214 ISBN 13: 9781108420211
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Gebunden. Zustand: New. With a machine learning approach and less focus on linguistic details, this natural language processing textbook introduces the fundamental mathematical and deep learning models for NLP in a unified framework. An invaluable, accessible and up-to-date tool f.
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In den WarenkorbHardcover. Zustand: Brand New. 470 pages. 9.75x7.25x1.05 inches. In Stock.
Sprache: Englisch
Verlag: Cambridge University Press, 2021
ISBN 10: 1108420214 ISBN 13: 9781108420211
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Buch. Zustand: Neu. Natural Language Processing | A Machine Learning Perspective | Yue Zhang (u. a.) | Buch | Gebunden | Englisch | 2021 | Cambridge University Press | EAN 9781108420211 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
Sprache: Englisch
Verlag: Cambridge University Press Jan 2021, 2021
ISBN 10: 1108420214 ISBN 13: 9781108420211
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Neuware - With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature, including classification, sequence labelling, and sequence-to-sequence problems. Topics covered include statistical machine learning and deep learning models, text classification and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based methods. Rich connections are drawn between concepts throughout the book, equipping students with the tools needed to establish a deep understanding of NLP solutions, adapt existing models, and confidently develop innovative models of their own. Featuring a host of examples, intuition, and end of chapter exercises, plus sample code available as an online resource, this textbook is an invaluable tool for the upper undergraduate and graduate student.