Shallow learning deep practical (4 risultati)

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  • Lingua: Inglese

    Editore: Springer, 2025

    3031695011 / 9783031695018

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    Da: preigu, Osnabrück, Germaniapreigu

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    EUR 140,10

    EUR 70,00 spedizione 
    Spedito da Germania a U.S.A.

    Quantità: 5 disponibili

    Taschenbuch. Condizione: Neu. Shallow Learning vs. Deep Learning | A Practical Guide for Machine Learning Solutions | Ömer Faruk Ertu¿rul (u. a.) | Taschenbuch | The Springer Series in Applied Machine Learning | xii | Englisch | 2025 | Springer | EAN 9783031695018 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

  • Lingua: Inglese

    Editore: Springer Nature, 2024

    3031694988 / 9783031694981

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    Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books

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    EUR 238,07

    EUR 14,58 spedizione 
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    Quantità: 2 disponibili

    Hardcover. Condizione: Brand New. 287 pages. 9.25x6.10x9.21 inches. In Stock.

  • Lingua: Inglese

    Editore: Springer, 2025

    3031695011 / 9783031695018

    • Brossura

    Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH

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    EUR 223,07

    EUR 30,50 spedizione 
    Spedito da Germania a U.S.A.

    Quantità: 1 disponibili

    Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book explores the ongoing debate between shallow and deep learning in the field of machine learning. It provides a comprehensive survey of machine learning methods, from shallow learning to deep learning, and examines their applications across various domains. Shallow Learning vs Deep Learning: A Practical Guide for Machine Learning Solutions emphasizes that the choice of a machine learning approach should be informed by the specific characteristics of the dataset, the operational environment, and the unique requirements of each application, rather than being influenced by prevailing trends.In each chapter, the book delves into different application areas, such as engineering, real-world scenarios, social applications, image processing, biomedical applications, anomaly detection, natural language processing, speech recognition, recommendation systems, autonomous systems, and smart grid applications. By comparing and contrasting the effectiveness of shallow and deep learning in these areas, the book provides a framework for thoughtful selection and application of machine learning strategies. This guide is designed for researchers, practitioners, and students who seek to deepen their understanding of when and how to apply different machine learning techniques effectively. Through comparative studies and detailed analyses, readers will gain valuable insights to make informed decisions in their respective fields.

  • Lingua: Inglese

    Editore: Springer, 2024

    3031694988 / 9783031694981

    • Rilegato

    Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH

    Venditore con 5 stelle
    Contatta il venditore

    Condizione: Nuovo

    EUR 224,71

    EUR 30,50 spedizione 
    Spedito da Germania a U.S.A.

    Quantità: 1 disponibili

    Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book explores the ongoing debate between shallow and deep learning in the field of machine learning. It provides a comprehensive survey of machine learning methods, from shallow learning to deep learning, and examines their applications across various domains. Shallow Learning vs Deep Learning: A Practical Guide for Machine Learning Solutions emphasizes that the choice of a machine learning approach should be informed by the specific characteristics of the dataset, the operational environment, and the unique requirements of each application, rather than being influenced by prevailing trends.In each chapter, the book delves into different application areas, such as engineering, real-world scenarios, social applications, image processing, biomedical applications, anomaly detection, natural language processing, speech recognition, recommendation systems, autonomous systems, and smart grid applications. By comparing and contrasting the effectiveness of shallow and deep learning in these areas, the book provides a framework for thoughtful selection and application of machine learning strategies. This guide is designed for researchers, practitioners, and students who seek to deepen their understanding of when and how to apply different machine learning techniques effectively. Through comparative studies and detailed analyses, readers will gain valuable insights to make informed decisions in their respective fields.