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Taschenbuch. Condition: Neu. Hands-On Implementation of NLP Models | Rupesh Kumar Tipu (u. a.) | Taschenbuch | 64 S. | Englisch | 2024 | LAP LAMBERT Academic Publishing | EAN 9786207806751 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.
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Published by LAP LAMBERT Academic Publishing Jun 2024, 2024
ISBN 10: 6207806751 ISBN 13: 9786207806751
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Published by LAP Lambert Academic Publishing, 2024
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Published by LAP LAMBERT Academic Publishing Jun 2024, 2024
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -'Hands-On Implementation of NLP Models' is a comprehensive guide designed to bridge the gap between theoretical concepts and practical applications in Natural Language Processing (NLP). The book covers fundamental NLP concepts, advanced models, and real-world applications.Chapters include an introduction to NLP, basics like text preprocessing and tokenization, classical models such as Naive Bayes and Support Vector Machines, and deep learning techniques including LSTM and transformer models like BERT and GPT. Practical chapters demonstrate building NLP models with Python, real-world case studies, and end-to-end project implementations.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 64 pp. Englisch.
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Published by LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6207806751 ISBN 13: 9786207806751
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 'Hands-On Implementation of NLP Models' is a comprehensive guide designed to bridge the gap between theoretical concepts and practical applications in Natural Language Processing (NLP). The book covers fundamental NLP concepts, advanced models, and real-world applications.Chapters include an introduction to NLP, basics like text preprocessing and tokenization, classical models such as Naive Bayes and Support Vector Machines, and deep learning techniques including LSTM and transformer models like BERT and GPT. Practical chapters demonstrate building NLP models with Python, real-world case studies, and end-to-end project implementations.