Deep Learning: Concepts and Architectures

Shyi-Ming Chen

ISBN 10: 3030317587 / ISBN 13: 9783030317584

Verlag: Springer International Publishing Nov 2020, 2020

Beschreibung

This item is printed on demand - it takes 3-4 days longer - Neuware -This book introduces readers to the fundamental concepts of deep learning and offers practical insights into how this learning paradigm supports automatic mechanisms of structural knowledge representation. It discusses a number of multilayer architectures giving rise to tangible and functionally meaningful pieces of knowledge, and shows how the structural developments have become essential to the successful delivery of competitive practical solutions to real-world problems. The book also demonstrates how the architectural developments, which arise in the setting of deep learning, support detailed learning and refinements to the system design. Featuring detailed descriptions of the current trends in the design and analysis of deep learning topologies, the book offers practical guidelines and presents competitive solutions to various areas of language modeling, graph representation, and forecasting. 356 pp. Englisch.

Bestandsnummer des Verkäufers 9783030317584

Bibliografische Details

• Titel: Deep Learning: Concepts and Architectures
• Verlag: Springer International Publishing Nov 2020
• Erscheinungsjahr: 2020
• Sprache: Englisch
• ISBN-10: 3030317587
• ISBN-13: 9783030317584
• Einband: Taschenbuch
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

This book introduces readers to the fundamental concepts of deep learning and offers practical insights into how this learning paradigm supports automatic mechanisms of structural knowledge representation. It discusses a number of multilayer architectures giving rise to tangible and functionally meaningful pieces of knowledge, and shows how the structural developments have become essential to the successful delivery of competitive practical solutions to real-world problems. The book also demonstrates how the architectural developments, which arise in the setting of deep learning, support detailed learning and refinements to the system design. Featuring detailed descriptions of the current trends in the design and analysis of deep learning topologies, the book offers practical guidelines and presents competitive solutions to various areas of language modeling, graph representation, and forecasting.