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Neural Network Learning: Theoretical Foundations Martin Anthony, Peter L. Bartlett
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This important work describes recent theoretical advances in the study of artificial neural networks. Product DescriptionThis important work describes recent theoretical advances in the study of artificial neural networks. Опубликовано 31st May пользователем Vadym Garbuzov. Share this I'm a bit of a freak – enterprise software team lead during the day and neural network researcher during the evening. 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. Cite as: arXiv:1303.0818 [cs.NE]. Learning theory (supervised/ unsupervised/ reinforcement learning) Knowledge based networks. 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. Cheap This important work describes recent theoretical advances in the study of artificial neural networks. Subjects: Neural and Evolutionary Computing (cs.NE); Information Theory (cs.IT); Learning (cs.LG); Differential Geometry (math.DG). 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. Artificial Neural Networks Mathematical foundations of neural networks. Ярлыки: tutorials djvu ebook hotfile epub chm filesonic rapidshare Tags:Neural Network Learning: Theoretical Foundations fileserve pdf downloads torrent book.