Deep Learning: Algorithms and Applications
Deep Learning: Algorithms and Applications
edited by Witold Pedrycz, Shyi-Ming Chen
- First edition
- 1 recurso en línea (XII, 360 páginas) 171 ilustraciones, 139 ilustraciones a color
- Studies in Computational Intelligence 865 1860-949X Intelligent Technologies and Robotics (Springer-42732) .
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.
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.
9783030317607
10.1007/978-3-030-31760-7 doi
Aprendizaje automático
Q325.5 / 2020 EB
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.
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.
9783030317607
10.1007/978-3-030-31760-7 doi
Aprendizaje automático
Q325.5 / 2020 EB