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Shallow Learning vs. Deep Learning

Shallow Learning vs. Deep Learning

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Description
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.
Product details
Binding:
Paperback
Number of Pages:
288
Release Date:
2025-10-13
Publication Date:
2025-10-13
Publisher:
Springer
Languages:
Original: English
ISBN10:
3031695011
ISBN13:
9783031695018
GPSR Manufacturer Reference:
[email protected]
Weight:
441 g
Height:
155 cm
Width:
235 cm
Thickness:
16 cm
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