Custom cover image
Custom cover image

Shallow Learning vs. Deep Learning : A Practical Guide for Machine Learning Solutions / edited by Ömer Faruk Ertuğrul, Josep M Guerrero, Musa Yilmaz

Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: The Springer Series in Applied Machine LearningPublisher: Cham : Springer Nature Switzerland, 2024Publisher: Cham : Imprint: Springer, 2024Edition: 1st ed. 2024Description: 1 Online-Ressource(XII, 275 p. 114 illus., 104 illus. in color.)ISBN:
  • 9783031694998
Subject(s): Additional physical formats: 9783031694981 | 9783031695001 | 9783031695018 | Erscheint auch als: 9783031694981 Druck-Ausgabe | Erscheint auch als: 9783031695001 Druck-Ausgabe | Erscheint auch als: 9783031695018 Druck-AusgabeDDC classification:
  • 006.31 23
DOI: DOI: 10.1007/978-3-031-69499-8Online resources: Summary: 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.Summary: 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.PPN: PPN: 1905700954Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-CWD | ZDB-2-SXPC
No physical items for this record
KIT – The Research University in the Helmholtz Association                         Home | Impressum | Datenschutz | Barrierefreiheit | KIT