Development and Analysis of Deep Learning Architectures
Development and Analysis of Deep Learning Architectures
edited by Witold Pedrycz, Shyi-Ming Chen.
- 1st ed. 2020.
- 1 recurso en línea (XI, 292 páginas) 135 ilustraciones, 120 ilustraciones a color.
- Studies in Computational Intelligence 867 1860-949X Intelligent Technologies and Robotics (Springer-42732) .
Preface -- Chapter 1. Direct Error Driven Learning for Classification in Applications Generating Big-Data -- Chapter 2. Deep Learning for Soft Sensor Design -- Chapter 3. Case Study: Deep Convolutional Networks in Healthcare, etc.
This book offers a timely reflection on the remarkable range of algorithms and applications that have made the area of deep learning so attractive and heavily researched today. Introducing the diversity of learning mechanisms in the environment of big data, and presenting authoritative studies in fields such as sensor design, health care, autonomous driving, industrial control and wireless communication, it enables readers to gain a practical understanding of design. The book also discusses systematic design procedures, optimization techniques, and validation processes.
9783030317645
10.1007/978-3-030-31764-5 doi
Aprendizaje automático
Q325.5 / 2020 EB
Preface -- Chapter 1. Direct Error Driven Learning for Classification in Applications Generating Big-Data -- Chapter 2. Deep Learning for Soft Sensor Design -- Chapter 3. Case Study: Deep Convolutional Networks in Healthcare, etc.
This book offers a timely reflection on the remarkable range of algorithms and applications that have made the area of deep learning so attractive and heavily researched today. Introducing the diversity of learning mechanisms in the environment of big data, and presenting authoritative studies in fields such as sensor design, health care, autonomous driving, industrial control and wireless communication, it enables readers to gain a practical understanding of design. The book also discusses systematic design procedures, optimization techniques, and validation processes.
9783030317645
10.1007/978-3-030-31764-5 doi
Aprendizaje automático
Q325.5 / 2020 EB