Da: Universitätsbuchhandlung Herta Hold GmbH, Berlin, Germania
EUR 21,00
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Aggiungi al carrelloXI, 284 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Intelligent Systems Reference Library, 186. Sprache: Englisch.
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 119,74
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 115,52
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Aggiungi al carrelloCondizione: New. In.
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 130,92
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 115,51
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 126,47
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Condizione: New.
Da: Revaluation Books, Exeter, Regno Unito
EUR 154,48
Quantità: 2 disponibili
Aggiungi al carrelloHardcover. Condizione: Brand New. 295 pages. 9.25x6.10x0.69 inches. In Stock.
Lingua: Inglese
Editore: Springer International Publishing, 2020
ISBN 10: 303042748X ISBN 13: 9783030427481
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 106,99
Quantità: 1 disponibili
Aggiungi al carrelloBuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces readers to the current trends in using deep learners and deep learner descriptors for medical applications. It reviews the recent literature and presents a variety of medical image and sound applications to illustrate the five major ways deep learners can be utilized: 1) by training a deep learner from scratch (chapters provide tips for handling imbalances and other problems with the medical data); 2) by implementing transfer learning from a pre-trained deep learner and extracting deep features for different CNN layers that can be fed into simpler classifiers, such as the support vector machine; 3) by fine-tuning one or more pre-trained deep learners on an unrelated dataset so that they are able to identify novel medical datasets; 4) by fusing different deep learner architectures; and 5) by combining the above methods to generate a variety of more elaborate ensembles. This book is a value resource for anyone involved in engineering deep learners for medical applications as well as to those interested in learning more about the current techniques in this exciting field. A number of chapters provide source code that can be used to investigate topics further or to kick-start new projects.
Da: Mispah books, Redhill, SURRE, Regno Unito
EUR 164,54
Quantità: 1 disponibili
Aggiungi al carrelloHardcover. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Da: Brook Bookstore On Demand, Napoli, NA, Italia
EUR 86,24
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Aggiungi al carrelloCondizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer International Publishing Mai 2020, 2020
ISBN 10: 303042748X ISBN 13: 9783030427481
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 106,99
Quantità: 2 disponibili
Aggiungi al carrelloBuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book introduces readers to the current trends in using deep learners and deep learner descriptors for medical applications. It reviews the recent literature and presents a variety of medical image and sound applications to illustrate the five major ways deep learners can be utilized: 1) by training a deep learner from scratch (chapters provide tips for handling imbalances and other problems with the medical data); 2) by implementing transfer learning from a pre-trained deep learner and extracting deep features for different CNN layers that can be fed into simpler classifiers, such as the support vector machine; 3) by fine-tuning one or more pre-trained deep learners on an unrelated dataset so that they are able to identify novel medical datasets; 4) by fusing different deep learner architectures; and 5) by combining the above methods to generate a variety of more elaborate ensembles. This book is a value resource for anyone involved in engineering deep learners for medical applications as well as to those interested in learning more about the current techniques in this exciting field. A number of chapters provide source code that can be used to investigate topics further or to kick-start new projects. 296 pp. Englisch.
Lingua: Inglese
Editore: Springer International Publishing, 2020
ISBN 10: 303042748X ISBN 13: 9783030427481
Da: moluna, Greven, Germania
EUR 92,27
Quantità: Più di 20 disponibili
Aggiungi al carrelloGebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents recent research on all aspects of machine learning and data mining for health care Focuses on general algorithms that can handle multiple sources of complex data in medical research databasesIncludes various successful machine lear.
Da: Majestic Books, Hounslow, Regno Unito
EUR 150,73
Quantità: 4 disponibili
Aggiungi al carrelloCondizione: New. Print on Demand.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 150,54
Quantità: 4 disponibili
Aggiungi al carrelloCondizione: New. PRINT ON DEMAND.
Lingua: Inglese
Editore: Springer, Springer Mai 2020, 2020
ISBN 10: 303042748X ISBN 13: 9783030427481
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 106,99
Quantità: 1 disponibili
Aggiungi al carrelloBuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book introduces readers to the current trends in using deep learners and deep learner descriptors for medical applications. It reviews the recent literature and presents a variety of medical image and sound applications to illustrate the five major ways deep learners can be utilized: 1) by training a deep learner from scratch (chapters provide tips for handling imbalances and other problems with the medical data); 2) by implementing transfer learning from a pre-trained deep learner and extracting deep features for different CNN layers that can be fed into simpler classifiers, such as the support vector machine; 3) by fine-tuning one or more pre-trained deep learners on an unrelated dataset so that they are able to identify novel medical datasets; 4) by fusing different deep learner architectures; and 5) by combining the above methods to generate a variety of more elaborate ensembles. This book is a value resource for anyone involved in engineering deep learners for medical applications as well as to those interested in learning more about the current techniques in this exciting field. A number of chapters provide source code that can be used to investigate topics further or to kick-start new projects.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 296 pp. Englisch.