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Development and Analysis of Deep Learning Architectures / edited by Witold Pedrycz, Shyi-Ming Chen.

Contributor(s): SpringerLink (Online service) | Pedrycz, Witold (1953-), editor | Chen, Shyi-Ming, editor
Material type: materialTypeLabelE-bookSeries: (Studies in Computational Intelligence, 1860-949X; 867); (Intelligent Technologies and Robotics (Springer-42732)).Publisher: Cham : Springer International Publishing : Imprint: Springer, 2020Edition: 1st ed. 2020.Description: 1 recurso en línea (XI, 292 páginas) : 135 ilustraciones, 120 ilustraciones a color..ISBN: 9783030317645.Subject: Aprendizaje automáticoOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
Contents:
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.
In: Springer eBooksAbstract: 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.
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Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias e Ingeniería Q325.5 2020 EB (Browse shelf(Opens below)) Acceso electrónico eBook04032111
Total holds: 0

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.

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