Deep Learning: Algorithms and Applications
- Length: 372 pages
- Edition: 1
- Language: English
- Publisher: Springer
- Publication Date: 2019-11-04
- ISBN-10: 3030317595
- ISBN-13: 9783030317591
- Sales Rank: #4038286 (See Top 100 Books)
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.
Cover Front Matter Activation Functions Adversarial Examples in Deep Neural Networks: An Overview Representation Learning in Power Time Series Forecasting Deep Learning Application: Load Forecasting in Big Data of Smart Grids Fast and Accurate Seismic Tomography via Deep Learning Traffic Light and Vehicle Signal Recognition with High Dynamic Range Imaging and Deep Learning The Application of Deep Learning in Marine Sciences Deep Learning Case Study on Imbalanced Training Data for Automatic Bird Identification Deep Learning for Person Re-identification in Surveillance Videos Deep Learning in Gait Analysis for Security and Healthcare Deep Learning for Building Occupancy Estimation Using Environmental Sensors Back Matter
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