Deep learners learner descriptors (3 Ergebnisse)

Sprache: Englisch
Verlag: Springer, 2020
Serie: Buch 156 von 188 - Intelligent Systems Reference Library
- Hardcover
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes KönigreichRia Christie Collections
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 116,50
EUR 13,99 VersandVersand von Vereinigtes Königreich nach USAAnzahl: Mehr als 20 verfügbar
Zustand: New. In.

Deep Learners and Deep Learner Descriptors for Medical Applications
Brahnam, Sheryl (Editor)/ Nanni, Loris (Editor)/ Jain, Lakhmi C. (Editor)
Sprache: Englisch
Verlag: Springer Nature, 2020
Serie: Buch 156 von 188 - Intelligent Systems Reference Library
- Hardcover
Anbieter: Revaluation Books, Exeter, Vereinigtes KönigreichRevaluation Books
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 155,67
EUR 14,59 VersandVersand von Vereinigtes Königreich nach USAAnzahl: 2 verfügbar
Hardcover. Zustand: Brand New. 295 pages. 9.25x6.10x0.69 inches. In Stock.

Sprache: Englisch
Verlag: Springer International Publishing, 2020
Serie: Buch 156 von 188 - Intelligent Systems Reference Library
- Hardcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 106,99
EUR 63,06 VersandVersand von Deutschland nach USAAnzahl: 1 verfügbar
Buch. Zustand: 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 way…s 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.