04557nam a22005655i 4500 978-3-031-69499-8 DE-He213 20251030113139.0 cr nn 008mamaa 241013s2024 sz | s |||| 0|eng d 9783031694998 978-3-031-69499-8 10.1007/978-3-031-69499-8 doi Q325.5-.7 UYQM bicssc MAT029000 bisacsh UYQM thema Shallow Learning vs. Deep Learning [electronic resource] : A Practical Guide for Machine Learning Solutions / edited by Ömer Faruk Ertuğrul, Josep M Guerrero, Musa Yilmaz. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XII, 274 p. 114 illus., 104 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda The Springer Series in Applied Machine Learning, 2520-1301 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. Machine learning. 55059 Artificial intelligence. 58441 Application software. Machine Learning. 55059 Artificial Intelligence. 58441 Computer and Information Systems Applications. Ertuğrul, Ömer Faruk. editor. edt http://id.loc.gov/vocabulary/relators/edt 85954 Guerrero, Josep M. editor. edt http://id.loc.gov/vocabulary/relators/edt 85515 Yilmaz, Musa. editor. edt http://id.loc.gov/vocabulary/relators/edt 85955 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031694981 Printed edition: 9783031695001 Printed edition: 9783031695018 The Springer Series in Applied Machine Learning, 2520-1301 84135 https://doi.org/10.1007/978-3-031-69499-8 ZDB-2-CWD ZDB-2-SXPC 64163 64163