ISBN: 9783031145971 - normalization techniques in deep learning (2 risultati)

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  • Lingua: Inglese

    Editore: Springer, 2023

    3031145976 / 9783031145971

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    Da: Buchpark, Trebbin, GermaniaBuchpark

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    Condizione: Usato - Ottimo

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    Prezzo corrente: EUR 47,01

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    Spedito da Germania a U.S.A.

    Quantità: 1 disponibile

    Condizione: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | ¿This book presents and surveys normalization techniques with a deep analysis in training deep neural networks. In addition, the author provides technical details in designing new normalization methods and network architectures tailored to specific tasks. Normalization methods can improve the training stability, optimization efficiency, and generalization ability of deep neural networks (DNNs) and have become basic components in most state-of-the-art DNN architectures. The author provides guidelines for elaborating, understanding, and applying normalization methods. This book is ideal for readers working on the development of novel deep learning algorithms and/or their applications to solve practical problems in computer vision and machine learning tasks. The book also serves as a resource researchers, engineers, and students who are new to the field and need to understand and train DNNs.…

  • Lingua: Inglese

    Editore: Springer, 2023

    3031145976 / 9783031145971

    • Brossura

    Da: Buchpark, Trebbin, GermaniaBuchpark

    Venditore con 5 stelle
    Contatta il venditore

    Condizione: Usato

    Affare

    Prezzo corrente: EUR 48,42

    EUR 105,00 spedizione 
    Spedito da Germania a U.S.A.

    Quantità: 2 disponibili

    Condizione: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | ¿This book presents and surveys normalization techniques with a deep analysis in training deep neural networks. In addition, the author provides technical details in designing new normalization methods and network architectures tailored to specific tasks. Normalization methods can improve the training stability, optimization efficiency, and generalization ability of deep neural networks (DNNs) and have become basic components in most state-of-the-art DNN architectures. The author provides guidelines for elaborating, understanding, and applying normalization methods. This book is ideal for readers working on the development of novel deep learning algorithms and/or their applications to solve practical problems in computer vision and machine learning tasks. The book also serves as a resource researchers, engineers, and students who are new to the field and need to understand and train DNNs.…