Normalization Techniques in Deep Learning
- Indbinding:
- Paperback
- Sideantal:
- 124
- Udgivet:
- 10. oktober 2023
- Udgave:
- 23001
- Størrelse:
- 168x8x240 mm.
- Vægt:
- 223 g.
- 8-11 hverdage.
- 7. december 2024
På lager
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Ingen binding og kan opsiges når som helst.
- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
Beskrivelse af Normalization Techniques in Deep Learning
¿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.
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Bogen Normalization Techniques in Deep Learning findes i følgende kategorier:
- Business og læring > Computer og IT
- Matematik og naturvidenskab > Matematik > Anvendt matematik > Matematisk modellering
- Databehandling og informationsteknologi > Informatik > Kunstig intelligens > Machine learning
- Databehandling og informationsteknologi > Informatik > Kunstig intelligens > Computer vision
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