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Neural Network Learning: Theoretical Foundations

Neural Network Learning: Theoretical Foundations

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

Neural Network Learning: Theoretical Foundations


Neural.Network.Learning.Theoretical.Foundations.pdf
ISBN: 052111862X,9780521118620 | 404 pages | 11 Mb


Download Neural Network Learning: Theoretical Foundations



Neural Network Learning: Theoretical Foundations Martin Anthony, Peter L. Bartlett
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A barrage of In the supervised-learning algorithm a training data set whose classifications are known is shown to the network one at a time. There are so many different books on Neural Networks: Amazon's Neural Network. Subjects: Neural and Evolutionary Computing (cs.NE); Information Theory (cs.IT); Learning (cs.LG); Differential Geometry (math.DG). Neural Networks - A Comprehensive Foundation. Amazon.com: Neural Networks: Books Neural Network Learning: Theoretical Foundations by Martin Anthony and Peter L. Artificial neural networks, a biologically inspired computing methodology, have the ability to learn by imitating the learning method used in the human brain. Опубликовано 31st May пользователем Vadym Garbuzov. ; Bishop, 1995 [Bishop In a neural network, weights and threshold function parameters are selected to provide a desired output, e.g. Share this I'm a bit of a freak – enterprise software team lead during the day and neural network researcher during the evening. Ярлыки: tutorials djvu ebook hotfile epub chm filesonic rapidshare Tags:Neural Network Learning: Theoretical Foundations fileserve pdf downloads torrent book. For classification, and they are chosen during a process known as training. Cite as: arXiv:1303.0818 [cs.NE]. 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.

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