ISBN: 9783031145971 - normalization techniques in deep learning (synthesis lectures on computer vision) (2 resultados)

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  • Idioma: Inglés

    Editorial: Springer, 2023

    3031145976 / 9783031145971

    • Tapa blanda

    Librería: Buchpark, Trebbin, AlemaniaBuchpark

    Vendedor de 5 estrellas
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    Condición: Usado - Excelente

    Oportunidad

    Precio actual: EUR 47,01

    Envío por EUR 105,00 
    Se envía de Alemania a Estados Unidos de America

    Cantidad disponible: 1 disponible

    Condición: 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.…

  • Idioma: Inglés

    Editorial: Springer, 2023

    3031145976 / 9783031145971

    • Tapa blanda

    Librería: Buchpark, Trebbin, AlemaniaBuchpark

    Vendedor de 5 estrellas
    Contactar con el vendedor

    Condición: Usado

    Oportunidad

    Precio actual: EUR 48,42

    Envío por EUR 105,00 
    Se envía de Alemania a Estados Unidos de America

    Cantidad disponible: 2 disponibles

    Condición: 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.…