Deep Learning: Algorithms and Applications

Shyi-Ming Chen

ISBN 10: 3030317625 / ISBN 13: 9783030317621

Verlag: Springer International Publishing, Springer International Publishing Nov 2020, 2020

Beschreibung

Neuware -This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm¿s algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 372 pp. Englisch.

Bestandsnummer des Verkäufers 9783030317621

Bibliografische Details

• Titel: Deep Learning: Algorithms and Applications
• Verlag: Springer International Publishing, Springer International Publishing Nov 2020
• Erscheinungsjahr: 2020
• Sprache: Englisch
• ISBN-10: 3030317625
• ISBN-13: 9783030317621
• Einband: Taschenbuch
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm’s algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.