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Shallow learning vs. deep learning = a practical guide for machine learning solutions /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Shallow learning vs. deep learning/ edited by Ömer Faruk Ertuğrul, Josep M Guerrero, Musa Yilmaz.
Reminder of title:	a practical guide for machine learning solutions /
other author:	Ertuğrul, Ömer Faruk.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	xii, 274 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-3-031-69499-8
ISBN:	9783031694998

Shallow learning vs. deep learning = a practical guide for machine learning solutions /
Shallow learning vs. deep learninga practical guide for machine learning solutions /[electronic resource] :edited by Ömer Faruk Ertuğrul, Josep M Guerrero, Musa Yilmaz. - Cham :Springer Nature Switzerland :2024. - xii, 274 p. :ill. (some col.), digital ;24 cm. - Springer series in applied machine learning,2520-1301. - Springer series in applied machine learning..

Survey of machine learning methods from shallow learning to deep learning -- Shallow learning vs Deep learning in engineering applications -- Shallow learning vs Deep learning in real-world applications -- Shallow learning vs Deep learning in social applications -- Shallow learning vs Deep learning in image processing applications -- Shallow learning vs Deep learning in biomedical applications -- Shallow learning vs Deep learning in anomaly detection applications -- Shallow learning vs Deep learning in natural language processing applications -- Shallow learning vs Deep learning in speech recognition applications -- Shallow learning vs Deep learning in recommendation systems -- Shallow learning vs Deep learning in autonomous systems -- Shallow Learning vs Deep Learning in Smart Grid Applications.

This book explores the ongoing debate between shallow and deep learning in the field of machine learning. It provides a comprehensive survey of machine learning methods, from shallow learning to deep learning, and examines their applications across various domains. Shallow Learning vs Deep Learning: A Practical Guide for Machine Learning Solutions emphasizes that the choice of a machine learning approach should be informed by the specific characteristics of the dataset, the operational environment, and the unique requirements of each application, rather than being influenced by prevailing trends. In each chapter, the book delves into different application areas, such as engineering, real-world scenarios, social applications, image processing, biomedical applications, anomaly detection, natural language processing, speech recognition, recommendation systems, autonomous systems, and smart grid applications. By comparing and contrasting the effectiveness of shallow and deep learning in these areas, the book provides a framework for thoughtful selection and application of machine learning strategies. This guide is designed for researchers, practitioners, and students who seek to deepen their understanding of when and how to apply different machine learning techniques effectively. Through comparative studies and detailed analyses, readers will gain valuable insights to make informed decisions in their respective fields.

ISBN: 9783031694998

Standard No.: 10.1007/978-3-031-69499-8doiSubjects--Topical Terms:

561253
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Shallow learning vs. deep learning = a practical guide for machine learning solutions /
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520 $a This book explores the ongoing debate between shallow and deep learning in the field of machine learning. It provides a comprehensive survey of machine learning methods, from shallow learning to deep learning, and examines their applications across various domains. Shallow Learning vs Deep Learning: A Practical Guide for Machine Learning Solutions emphasizes that the choice of a machine learning approach should be informed by the specific characteristics of the dataset, the operational environment, and the unique requirements of each application, rather than being influenced by prevailing trends. In each chapter, the book delves into different application areas, such as engineering, real-world scenarios, social applications, image processing, biomedical applications, anomaly detection, natural language processing, speech recognition, recommendation systems, autonomous systems, and smart grid applications. By comparing and contrasting the effectiveness of shallow and deep learning in these areas, the book provides a framework for thoughtful selection and application of machine learning strategies. This guide is designed for researchers, practitioners, and students who seek to deepen their understanding of when and how to apply different machine learning techniques effectively. Through comparative studies and detailed analyses, readers will gain valuable insights to make informed decisions in their respective fields.
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