Deep Learning: Concepts and Architectures

Shyi-Ming Chen

ISBN 10: 3030317587 / ISBN 13: 9783030317584

Editore: Springer International Publishing, Springer International Publishing Nov 2020, 2020

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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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 356 pp. Englisch.

Codice articolo 9783030317584

Dati bibliografici

• Titolo: Deep Learning: Concepts and Architectures
• Editore: Springer International Publishing, Springer International Publishing Nov 2020
• Data di pubblicazione: 2020
• Lingua: Inglese
• ISBN 10: 3030317587
• ISBN 13: 9783030317584
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 20 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 20 millimetri di profondità
• Peso: 540 grammi

Descrizione articolo

Riassunto

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.