Deep Learning: Concepts and Architectures

1. Auflage 2020
ISBN: 978-3-030-31755-3
Verlag: Springer International Publishing

Buch, Englisch, Band 866, 342 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 699 g

Reihe: Studies in Computational Intelligence

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.

Zielgruppe

Research

Autoren/Hrsg.

Chen, Shyi-Ming

Pedrycz, Witold

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

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.