Neural Network Learning: Theoretical Foundations by Martin Anthony, Peter L. Bartlett

Neural Network Learning: Theoretical Foundations



Neural Network Learning: Theoretical Foundations book download




Neural Network Learning: Theoretical Foundations Martin Anthony, Peter L. Bartlett ebook
Publisher:
Page: 404
Format: pdf
ISBN: 052111862X, 9780521118620


Underlying this need is the concept of “ connectionism”, which is concerned with the computational and learning capabilities of assemblies of simple processors, called artificial neural networks. Cite as: arXiv:1303.0818 [cs.NE]. The artificial neural networks, which represent the electrical analogue of the biological nervous systems, are gaining importance for their increasing applications in supervised (parametric) learning problems. For beginners it is a nice introduction to the subject, for experts a valuable reference. Subjects: Neural and Evolutionary Computing (cs.NE); Information Theory (cs.IT); Learning (cs.LG); Differential Geometry (math.DG). 'The book is a useful and readable mongraph. There are so many different books on Neural Networks: Amazon's Neural Network. My guess is that these patterns will not only be useful for machine learning, but also any other computational work that involves either a) processing large amounts of data, or b) algorithms that take a significant amount of time to execute. Amazon.com: Neural Networks: Books Neural Network Learning: Theoretical Foundations by Martin Anthony and Peter L. Share this I'm a bit of a freak – enterprise software team lead during the day and neural network researcher during the evening. Download free ebooks rapidshare, usenet,bittorrent. Download free Neural Networks and Computational Complexity (Progress in Theoretical Computer Science) H.