Deep Learning: Algorithms and Applications

Witold Pedrycz

ISBN 10: 3030317595 / ISBN 13: 9783030317591

Published by Springer, Palgrave Macmillan Nov 2019, 2019

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm¿s algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 372 pp. Englisch.

Seller Inventory # 9783030317591

Bibliographic Details

• Title: Deep Learning: Algorithms and Applications
• Publisher: Springer, Palgrave Macmillan Nov 2019
• Publication Date: 2019
• Language: English
• ISBN 10: 3030317595
• ISBN 13: 9783030317591
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 26D millimeters 241 millimeters width by 160 millimeters height by 26 millimeters depth
• Weight: 723 grams

About this title

Synopsis

This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm’s algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.