Random Matrix Methods Machine by Couillet Romain (17 results)
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Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Condition: New. New edition NO-PA16APR2015-KAP.
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- Hardcover
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- Hardcover
Seller: Rarewaves.com USA, London, LONDO, United KingdomRarewaves.com USA
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US$ 136.57
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Hardback. Condition: New. 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 mach…ine 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.
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Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Hardcover. Condition: Brand New. 450 pages. 9.96x6.97x0.91 inches. In Stock.
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Seller: moluna, Greven, Germanymoluna
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- Hardcover
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Condition: New. 2022. New. hardcover. . . . . . Books ship from the US and Ireland.
- Hardcover
Seller: Rarewaves.com UK, London, United KingdomRarewaves.com UK
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US$ 131.78
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Hardback. Condition: New. 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 mach…ine 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.
- Hardcover
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrelandKennys Bookshop and Art Galleries Ltd.
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Condition: New. 2022. New. hardcover. . . . . .
- Hardcover
- Print on Demand
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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US$ 108.61
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Hardcover. Condition: Brand New. 450 pages. 9.96x6.97x0.91 inches. In Stock. This item is printed on demand.
- Hardcover
- Print on Demand
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
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US$ 114.76
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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 rea…l-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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
