Editore: Cambridge University Press, 2022
ISBN 10: 1009123238 ISBN 13: 9781009123235
Lingua: Inglese
Da: Basi6 International, Irving, TX, U.S.A.
EUR 75,62
Convertire valutaQuantità: 3 disponibili
Aggiungi al carrelloCondizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
Editore: Cambridge University Press, 2022
ISBN 10: 1009123238 ISBN 13: 9781009123235
Lingua: Inglese
Da: PBShop.store US, Wood Dale, IL, U.S.A.
EUR 85,34
Convertire valutaQuantità: 15 disponibili
Aggiungi al carrelloHRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
Editore: Cambridge University Press, 2022
ISBN 10: 1009123238 ISBN 13: 9781009123235
Lingua: Inglese
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
EUR 85,81
Convertire valutaQuantità: 15 disponibili
Aggiungi al carrelloHRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
Editore: Cambridge University Press, 2022
ISBN 10: 1009123238 ISBN 13: 9781009123235
Lingua: Inglese
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 82,94
Convertire valutaQuantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
Editore: Cambridge University Press, 2022
ISBN 10: 1009123238 ISBN 13: 9781009123235
Lingua: Inglese
Da: Majestic Books, Hounslow, Regno Unito
EUR 90,39
Convertire valutaQuantità: 3 disponibili
Aggiungi al carrelloCondizione: New.
Editore: Cambridge University Press, 2022
ISBN 10: 1009123238 ISBN 13: 9781009123235
Lingua: Inglese
Da: California Books, Miami, FL, U.S.A.
EUR 93,44
Convertire valutaQuantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
Editore: Cambridge University Press, 2022
ISBN 10: 1009123238 ISBN 13: 9781009123235
Lingua: Inglese
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 91,33
Convertire valutaQuantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New. In.
Editore: Cambridge University Press, 2022
ISBN 10: 1009123238 ISBN 13: 9781009123235
Lingua: Inglese
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 85,80
Convertire valutaQuantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
Editore: Cambridge University Press, 2022
ISBN 10: 1009123238 ISBN 13: 9781009123235
Lingua: Inglese
Da: Revaluation Books, Exeter, Regno Unito
EUR 92,49
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloHardcover. Condizione: Brand New. 450 pages. 9.96x6.97x0.91 inches. In Stock.
Editore: Cambridge University Press, 2022
ISBN 10: 1009123238 ISBN 13: 9781009123235
Lingua: Inglese
Da: Books Puddle, New York, NY, U.S.A.
EUR 97,08
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloCondizione: New. New edition NO-PA16APR2015-KAP.
Editore: Cambridge University Press, 2022
ISBN 10: 1009123238 ISBN 13: 9781009123235
Lingua: Inglese
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 92,36
Convertire valutaQuantità: Più di 20 disponibili
Aggiungi al carrelloHardback. Condizione: New. New copy - Usually dispatched within 4 working days. 872.
Editore: Cambridge University Press, 2022
ISBN 10: 1009123238 ISBN 13: 9781009123235
Lingua: Inglese
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 99,11
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloCondizione: New.
Editore: Cambridge University Press, 2022
ISBN 10: 1009123238 ISBN 13: 9781009123235
Lingua: Inglese
Da: moluna, Greven, Germania
EUR 97,40
Convertire valutaQuantità: Più di 20 disponibili
Aggiungi al carrelloGebunden. Condizione: New.
Editore: Cambridge University Press, 2022
ISBN 10: 1009123238 ISBN 13: 9781009123235
Lingua: Inglese
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 98,19
Convertire valutaQuantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Editore: Cambridge University Press, 2022
ISBN 10: 1009123238 ISBN 13: 9781009123235
Lingua: Inglese
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 99,19
Convertire valutaQuantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Editore: Cambridge University Press, Cambridge, 2022
ISBN 10: 1009123238 ISBN 13: 9781009123235
Lingua: Inglese
Da: CitiRetail, Stevenage, Regno Unito
EUR 97,12
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloHardcover. Condizione: 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 our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Editore: Cambridge University Press, 2022
ISBN 10: 1009123238 ISBN 13: 9781009123235
Lingua: Inglese
Da: Revaluation Books, Exeter, Regno Unito
EUR 126,64
Convertire valutaQuantità: 2 disponibili
Aggiungi al carrelloHardcover. Condizione: Brand New. 450 pages. 9.96x6.97x0.91 inches. In Stock.
Editore: Cambridge University Press, Cambridge, 2022
ISBN 10: 1009123238 ISBN 13: 9781009123235
Lingua: Inglese
Da: AussieBookSeller, Truganina, VIC, Australia
EUR 116,86
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloHardcover. Condizione: 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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Editore: Cambridge University Press, 2022
ISBN 10: 1009123238 ISBN 13: 9781009123235
Lingua: Inglese
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 82,86
Convertire valutaQuantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
Editore: Cambridge University Press Jul 2022, 2022
ISBN 10: 1009123238 ISBN 13: 9781009123235
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 133,58
Convertire valutaQuantità: 2 disponibili
Aggiungi al carrelloBuch. Condizione: Neu. Neuware - 'Numerous and large dimensional data is now a default setting in modern machine learning (ML). Standard ML algorithms, starting with kernel methods such as support vector machines and graph-based methods like the PageRank algorithm, were however initially designed out of small dimensional intuitions and tend to misbehave, if not completely collapse, when dealing with real-world large datasets. Random matrix theory has recently developed a broad spectrum of tools to help understand this new curse of dimensionality, to help repair or completely recreate the sub-optimal algorithms, and most importantly to provide new intuitions to deal with modern data mining'--.
Editore: Cambridge University Press, Cambridge, 2022
ISBN 10: 1009123238 ISBN 13: 9781009123235
Lingua: Inglese
Da: Grand Eagle Retail, Fairfield, OH, U.S.A.
EUR 100,27
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloHardcover. Condizione: 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.