Circuit Complexity and Neural Networks High quality books
Circuit Complexity and Neural Networks
By:Ian Parberry,Michael R. Garey,Albert Meyer
Published on 1994 by MIT Press
Neural networks usually work adequately on small problems but can run into trouble when they are scaled up to problems involving large amounts of input data. Circuit Complexity and Neural Networks addresses the important question of how well neural networks scale - that is, how fast the computation time and number of neurons grow as the problem size increases. It surveys recent research in circuit complexity (a robust branch of theoretical computer science) and applies this work to a theoretical understanding of the problem of scalability. Most research in neural networks focuses on learning, yet it is important to understand the physical limitations of the network before the resources needed to solve a certain problem can be calculated. One of the aims of this book is to compare the complexity of neural networks and the complexity of conventional computers, looking at the computational ability and resources (neurons and time) that are a necessary part of the foundations of neural network learning. Circuit Complexity and Neural Networks contains a significant amount of background material on conventional complexity theory that will enable neural network scientists to learn about how complexity theory applies to their discipline, and allow complexity theorists to see how their discipline applies to neural networks.
This Book was ranked at 12 by Google Books for keyword circuit.
Book ID of Circuit Complexity and Neural Networks's Books is Mc3sIKru91wC, Book which was written byIan Parberry,Michael R. Garey,Albert Meyerhave ETAG "DbwZqm1JwoI"
Book which was published by MIT Press since 1994 have ISBNs, ISBN 13 Code is 9780262161480 and ISBN 10 Code is 0262161486
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