Concepts for Neural Networks, Lawrence J. Landau

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
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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: 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
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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: Gurney, Kevin
Название: Introduction to neural networks
ISBN: 1857285034 ISBN-13(EAN): 9781857285031
Издательство: Taylor&Francis
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Цена: 71450.00 T
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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
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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.

Автор: Dominic Palmer-Brown; Chrisina Draganova; Elias Pi
Название: Engineering Applications of Neural Networks
ISBN: 3642039685 ISBN-13(EAN): 9783642039683
Издательство: Springer
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Цена: 116450.00 T
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Описание: A cursory glance at the table of contents of EANN 2009 reveals the am- ing range of neural network and related applications. Approximately 20% of submitted - pers will be chosen, the best according to the reviews, to be extended and - viewedagainfor inclusionin a specialissueofthe journalNeural Computing and Applications.

Автор: Leonardo Franco; Jos? M. Jerez
Название: Constructive Neural Networks
ISBN: 3642261086 ISBN-13(EAN): 9783642261084
Издательство: Springer
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Цена: 194730.00 T
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Описание: Constructive neural networks and other incremental learning algorithms are discussed in this volume as alternatives to methods for assessing adequate architectures. A valuable overview of the field is presented, in addition to useful applications.

Автор: Alex Graves
Название: Supervised Sequence Labelling with Recurrent Neural Networks
ISBN: 3642432182 ISBN-13(EAN): 9783642432187
Издательство: Springer
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Цена: 104480.00 T
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Описание: This book offers a complete framework for classifying and transcribing sequential data with recurrent neural networks. It uses state-of-the-art results in speech and handwriting recognition to show the framework in action.

Автор: Akira Hirose
Название: Complex-Valued Neural Networks
ISBN: 3642435793 ISBN-13(EAN): 9783642435799
Издательство: Springer
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Цена: 104480.00 T
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Описание: In its revised, expanded 2nd edition, this book provides a foundation for the study of complex-valued neural networks. The author emphasizes basic ideas and ways of thinking, and offers meaningful hints on using neural networks in fully practical applications.

Автор: Huajin Tang; Kay Chen Tan; Zhang Yi
Название: Neural Networks: Computational Models and Applications
ISBN: 3540692258 ISBN-13(EAN): 9783540692256
Издательство: Springer
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Цена: 158380.00 T
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Описание: Presents theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence.

Название: A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems
ISBN: 1849968675 ISBN-13(EAN): 9781849968676
Издательство: Springer
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Цена: 156720.00 T
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Описание: How does a machine learn a new concept on the basis of examples? This second edition takes account of important new developments in the field. It also deals extensively with the theory of learning control systems, now comparably mature to learning of neural networks.

Автор: Graupe Daniel
Название: Deep Learning Neural Networks: Design And Case Studies
ISBN: 9813146451 ISBN-13(EAN): 9789813146457
Издательство: World Scientific Publishing
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Цена: 42240.00 T
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Описание:

Deep Learning Neural Networks is the fastest growing field in machine learning. It serves as a powerful computational tool for solving prediction, decision, diagnosis, detection and decision problems based on a well-defined computational architecture. It has been successfully applied to a broad field of applications ranging from computer security, speech recognition, image and video recognition to industrial fault detection, medical diagnostics and finance.

This comprehensive textbook is the first in the new emerging field. Numerous case studies are succinctly demonstrated in the text. It is intended for use as a one-semester graduate-level university text and as a textbook for research and development establishments in industry, medicine and financial research.

Автор: Luo Fa-Long
Название: Applied Neural Networks for Signal Processing
ISBN: 0521644003 ISBN-13(EAN): 9780521644006
Издательство: Cambridge Academ
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Цена: 66530.00 T
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Описание: A comprehensive introduction to the use of neural networks in signal processing, covering basic principles and practical implementation procedures. A key feature of the book is that many carefully designed simulation examples are included to help guide the reader in the development of systems for new applications.