Deep Learning: Concepts and Architectures (Hardcover, 1st ed. 2020)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Studies in Computational Intelligence, 866
Release date:	November 2019
First published:	2020
Editors:	Witold Pedrycz • Shyi-Ming Chen
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	342
Edition:	1st ed. 2020
ISBN-13:	978-3-03-031755-3
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-03-031755-2
Barcode:	9783030317553

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