Items related to Random Matrix Methods for Machine Learning
- PublisherCambridge University Press
- Publication date2022
- ISBN 10 1009123238
- ISBN 13 9781009123235
- BindingHardcover
- LanguageEnglish
- Number of pages408
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Random Matrix Methods for Machine Learning
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Cambridge University Press, 2022
ISBN 10: 1009123238
ISBN 13: 9781009123235
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Random Matrix Methods for Machine Learning
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Cambridge University Press, 2022
ISBN 10: 1009123238
ISBN 13: 9781009123235
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Random Matrix Methods for Machine Learning
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ISBN 13: 9781009123235
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Random Matrix Methods for Machine Learning
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Random Matrix Methods for Machine Learning
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ISBN 13: 9781009123235
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Random Matrix Methods for Machine Learning
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ISBN 10: 1009123238
ISBN 13: 9781009123235
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Random Matrix Methods for Machine Learning
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Cambridge University Press, 2022
ISBN 10: 1009123238
ISBN 13: 9781009123235
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Random Matrix Methods for Machine Learning
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ISBN 13: 9781009123235
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Random Matrix Methods for Machine Learning
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Cambridge University Press, 2022
ISBN 10: 1009123238
ISBN 13: 9781009123235
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Random Matrix Methods for Machine Learning (Hardcover)
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ISBN 10: 1009123238
ISBN 13: 9781009123235
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Hardcover. Condition: new. Hardcover. This book presents a unified theory of random matrices for applications in machine learning, offering a large-dimensional data vision that exploits concentration and universality phenomena. This enables a precise understanding, and possible improvements, of the core mechanisms at play in real-world machine learning algorithms. The book opens with a thorough introduction to the theoretical basics of random matrices, which serves as a support to a wide scope of applications ranging from SVMs, through semi-supervised learning, unsupervised spectral clustering, and graph methods, to neural networks and deep learning. For each application, the authors discuss small- versus large-dimensional intuitions of the problem, followed by a systematic random matrix analysis of the resulting performance and possible improvements. All concepts, applications, and variations are illustrated numerically on synthetic as well as real-world data, with MATLAB and Python code provided on the accompanying website. For graduate students, practitioners, and sophisticated users, this book offers a tutorial approach to the foundations of random matrix theory for machine learning and systematic analyses of advanced applications ranging from power detection to deep neural networks. MATLAB and Python code is provided for all concepts and applications. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781009123235
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