Deep Learning: Concepts and Architectures

Witold Pedrycz, Shyi-Ming Chen (Herausgeber)

Buch | Hardcover

XII, 342 Seiten

2019
Springer International Publishing (Verlag)
978-3-030-31755-3 (ISBN)

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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.

Preface.- Chapter 1. Deep Learning Architectures.- Chapter 2. Theoretical Characterization of Deep Neural Networks.- Chapter 3. Scaling Analysis of Specialized Tensor Processing Architectures for Deep Learning Models, etc.

Erscheinungsdatum	15.11.2019
Reihe/Serie	Studies in Computational Intelligence
Zusatzinfo	XII, 342 p. 135 illus., 95 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	699 g
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Themenwelt	Technik
Schlagworte	AI • Computational Intelligence • computer vision • Deep Learning Architectures • Development Paradigms • Image Processing • Knowledge Representation • machine learning • Natural Language Processing • pattern recognition
ISBN-10	3-030-31755-2 / 3030317552
ISBN-13	978-3-030-31755-3 / 9783030317553
Zustand	Neuware