Deep Learning: Algorithms and Applications

Shyi-Ming Chen

ISBN 10: 3030317625 / ISBN 13: 9783030317621

Published by Springer International Publishing, 2020

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783030317621

Bibliographic Details

• Title: Deep Learning: Algorithms and Applications
• Publisher: Springer International Publishing
• Publication Date: 2020
• Language: English
• ISBN 10: 3030317625
• ISBN 13: 9783030317621
• Binding: Taschenbuch
• Condition: Neu

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.