Deep Learning: Algorithms and Applications

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.

List of contents

Preface.- Chapter 1. Activation Functions.- Chapter 2. Adversarial Examples in Deep Neural Networks: An Overview.- Chapter 3. Representation Learning in Power Time Series Forecasting, etc.

Product details

• Assisted by: Chen (Editor), Chen (Editor), Shyi-Ming Chen (Editor), Witol Pedrycz (Editor), Witold Pedrycz (Editor)
• Publisher: Springer, Berlin
• Languages: English
• Product format: Paperback / Softback
• Released: 18.11.2020
• EAN: 9783030317621
• ISBN: 978-3-0-3031762-1
• No. of pages: 360
• Dimensions: 155 mm x 20 mm x 235 mm
• Illustrations: XII, 360 p. 171 illus., 139 illus. in color.
• Series: Studies in Computational Intelligence
• Subject: Natural sciences, medicine, IT, technology > Technology > General, dictionaries