Development and Analysis of Deep Learning Architectures

1. Auflage 2020
ISBN: 978-3-030-31763-8
Verlag: Springer International Publishing

Buch, Englisch, Band 867, 292 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 623 g

Reihe: Studies in Computational Intelligence

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.

Zielgruppe

Research

Autoren/Hrsg.

Chen, Shyi-Ming

Pedrycz, Witold

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

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.