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Neural Network Learning: Theoretical Foundations Martin Anthony, Peter L. Bartlett
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Subjects: Neural and Evolutionary Computing (cs.NE); Information Theory (cs.IT); Learning (cs.LG); Differential Geometry (math.DG). Artificial neural networks, a biologically inspired computing methodology, have the ability to learn by imitating the learning method used in the human brain. For beginners it is a nice introduction to the subject, for experts a valuable reference. This important work describes recent theoretical advances in the study of artificial neural networks. Опубликовано 31st May пользователем Vadym Garbuzov. Cheap This important work describes recent theoretical advances in the study of artificial neural networks. Ярлыки: tutorials djvu ebook hotfile epub chm filesonic rapidshare Tags:Neural Network Learning: Theoretical Foundations fileserve pdf downloads torrent book. Neural Network Learning: Theoretical Foundations: Martin Anthony. Share this I'm a bit of a freak – enterprise software team lead during the day and neural network researcher during the evening. 10th International Conference on Inductive Logic Programming,. Learning theory (supervised/ unsupervised/ reinforcement learning) Knowledge based networks. 'The book is a useful and readable mongraph. Although this blog includes links to other Internet sites, it takes no responsibility for the content or information contained on those other sites, nor does it exert any editorial or other control over those other sites. Cite as: arXiv:1303.0818 [cs.NE]. Artificial Neural Networks Mathematical foundations of neural networks. 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.