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Deep learning for data analytics : foundations, biomedical applications and challenges
Das H., Pradhan C., Dey N., ACADEMIC PRESS, London, UK, 2020. 204 pp. Type: Book (978-0-128197-64-6)
Date Reviewed: Apr 15 2021

Deep learning for data analytics is composed of nine chapters and an index. It provides well-developed individual chapters with good sets of references. However, like most edited books, since each chapter is written by different authors, it is generally hard to develop fully integrated chapters. This book is a good example of an independently good but disconnected set of chapters.

It seems to me that the book can be used by researchers interested in the area of deep learning. It covers data analytics methods aimed to solve complex real-world problems, for example, in the areas of biomedical engineering and medical image recognition. Classical topics using deep learning methodologies are also covered. The editors’ good preface addresses the content of each chapter in a few substantive sentences.

The chapter titles are descriptive, so no need to provide an additional summary in this review. The back cover also provides a good summary:

The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinformatics, audio recognition, drug design, and medical image analysis.

Although the book’s stated goal is not fully achieved, there is enough information for initiated readers in specific application areas to benefit. In summary, I recommend the book to people who have some level of experience in the areas covered. It is not a book for beginners.

Reviewer:  M. M. Tanik Review #: CR147241 (2108-0202)
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