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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.
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.
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.
| Erscheinungsjahr: | 2024 |
|---|---|
| Genre: | Informatik, Mathematik, Medizin, Naturwissenschaften, Technik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Inhalt: |
xii
274 S. 10 s/w Illustr. 104 farbige Illustr. 274 p. 114 illus. 104 illus. in color. |
| ISBN-13: | 9783031694981 |
| ISBN-10: | 3031694988 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Redaktion: |
Ertu¿rul, Ömer Faruk
Yilmaz, Musa Guerrero, Josep M |
| Herausgeber: | Ömer Faruk Ertugrul/Josep M Guerrero/Musa Yilmaz |
| Hersteller: |
Springer Nature Switzerland
Springer International Publishing |
| Verantwortliche Person für die EU: | Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com |
| Maße: | 241 x 160 x 22 mm |
| Von/Mit: | Ömer Faruk Ertu¿rul (u. a.) |
| Erscheinungsdatum: | 13.10.2024 |
| Gewicht: | 0,598 kg |
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.
| Erscheinungsjahr: | 2024 |
|---|---|
| Genre: | Informatik, Mathematik, Medizin, Naturwissenschaften, Technik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Inhalt: |
xii
274 S. 10 s/w Illustr. 104 farbige Illustr. 274 p. 114 illus. 104 illus. in color. |
| ISBN-13: | 9783031694981 |
| ISBN-10: | 3031694988 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Redaktion: |
Ertu¿rul, Ömer Faruk
Yilmaz, Musa Guerrero, Josep M |
| Herausgeber: | Ömer Faruk Ertugrul/Josep M Guerrero/Musa Yilmaz |
| Hersteller: |
Springer Nature Switzerland
Springer International Publishing |
| Verantwortliche Person für die EU: | Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com |
| Maße: | 241 x 160 x 22 mm |
| Von/Mit: | Ömer Faruk Ertu¿rul (u. a.) |
| Erscheinungsdatum: | 13.10.2024 |
| Gewicht: | 0,598 kg |
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