ISBN: 9781108420211 - natural language processing: a machine learning perspective (6 Ergebnisse)

Zustand: New.

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.

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.…

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.…

Hardcover. Zustand: new. Hardcover. 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. 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 for the upper undergraduate and graduate student, with sample code available online. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Hardcover. Zustand: Brand New. 470 pages. 9.75x7.25x1.05 inches. In Stock.