Neural Networks and Qualitative Physics, Aubin

Автор: Brian D. Ripley
Название: Pattern Recognition and Neural Networks
ISBN: 0521717701 ISBN-13(EAN): 9780521717700
Издательство: Cambridge Academ
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Цена: 51750.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This 1996 book is a reliable account of the statistical framework for pattern recognition and machine learning. Valuable advice is included on both theory and applications, while case studies based on real data sets help readers develop their understanding. All data sets are available from www.stats.ox.ac.uk/~ripley/PRbook/

Автор: Bishop, Christopher M.
Название: Neural Networks for Pattern Recognition
ISBN: 0198538642 ISBN-13(EAN): 9780198538646
Издательство: Oxford Academ
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Цена: 92930.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is the first to provide a comprehensive account of neural networks from a statistical perspective. Its emphasis is on pattern recognition, which currently represents the area of greatest applicability for neural networks. By focusing on pattern recognition, the book provides a much more extensive treatment of many topics than is available in earlier books.

Автор: Gurney, Kevin
Название: Introduction to neural networks
ISBN: 1857285034 ISBN-13(EAN): 9781857285031
Издательство: Taylor&Francis
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Цена: 71450.00 T
Наличие на складе: Невозможна поставка.
Описание: Although mathematical ideas underpin the study of neural networks, this book presents the fundamentals without the full mathematical apparatus. It tackles various aspects of the field, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; and gradient descent methods.

Автор: Venkatesan, Ragav,
Название: Convolutional Neural Networks In Vi
ISBN: 1498770398 ISBN-13(EAN): 9781498770392
Издательство: Taylor&Francis
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Цена: 168430.00 T
Наличие на складе: Невозможна поставка.
Описание: This book covers the fundamentals in designing and deploying techniques using deep architectures. It is intended to serve as a beginner`s guide to engineers or students who want to have a quick start on learning and/or building deep learning systems.