Deep Learning: Concepts and Architectures
Published on 29. October 2019
XII, 342 pages
978-3-030-31756-0 (ISBN)
Description
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.
More details
Series
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
XII, 342 p. 135 illus., 95 illus. in color.
File size
13,33 MB
ISBN-13
978-3-030-31756-0 (9783030317560)
DOI
10.1007/978-3-030-31756-0
Schweitzer Classification
Other editions
Additional editions
Witold Pedrycz | Shyi-Ming Chen
Deep Learning: Concepts and Architectures
Book
11/2019
Springer
€171.19
Shipment within 7-9 days
Content
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.
