Machine Learning Paradigms: Advances in Deep Learning-based Technological Applications

Tsihrintzis, George A. (Editor)/ Jain, Lakhmi C. (Editor)

ISBN 10: 3030497267 / ISBN 13: 9783030497262

Edité par Springer-Nature New York Inc, 2021

A propos de cet article

442 pages. 9.25x6.10x1.05 inches. In Stock.

N° de réf. du vendeur x-3030497267

Détails bibliographiques

• Titre: Machine Learning Paradigms: Advances in Deep Learning-based Technological Applications
• Éditeur: Springer-Nature New York Inc
• Date de parution: 2021
• Langue: anglais
• ISBN à 10 chiffres: 3030497267
• ISBN à 13 chiffres: 9783030497262
• Reliure: Paperback
• État de l'article: Brand New

A propos de ce titre

Synopsis

At the dawn of the 4^th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep Learning in Systems Control, (5) Deep Learning in Feature Vector Processing, and (6) Evaluation of Algorithm Performance. 

This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.