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Data science : theory, algorithms, and applications / Gyanendra K. Verma, Badal Soni, Salah Bourennane, Alexandre C.B. Ramos, editors.

Verma, Gyanendra K., editor.; Soni, Badal, editor.; Bourennane, Salah, editor.; Ramos, Alexandre C. B., editor.
2021
QA76.87 .D38 2021
Available Online
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Title
Data science : theory, algorithms, and applications / Gyanendra K. Verma, Badal Soni, Salah Bourennane, Alexandre C.B. Ramos, editors.
ISBN
9789811616815 (electronic bk.)
9811616817 (electronic bk.)
9789811616808
9811616809
DOI
https://doi.org/10.1007/978-981-16-1681-5
Published
Singapore : Springer, [2021]
Copyright
©2021
Language
English
Description
1 online resource (444 pages) : illustrations (some color).
Item Number
10.1007/978-981-16-1681-5 doi
Call Number
QA76.87 .D38 2021
Dewey Decimal Classification
006.3/2
Summary
This book targets an audience with a basic understanding of deep learning, its architectures, and its application in the multimedia domain. Background in machine learning is helpful in exploring various aspects of deep learning. Deep learning models have a major impact on multimedia research and raised the performance bar substantially in many of the standard evaluations. Moreover, new multi-modal challenges are tackled, which older systems would not have been able to handle. However, it is very difficult to comprehend, let alone guide, the process of learning in deep neural networks, there is an air of uncertainty about exactly what and how these networks learn. By the end of the book, the readers will have an understanding of different deep learning approaches, models, pre-trained models, and familiarity with the implementation of various deep learning algorithms using various frameworks and libraries.
Access Note
Access limited to authorized users.
Source of Description
Description based on print version record.
Added Author
Verma, Gyanendra K., editor.
Soni, Badal, editor.
Bourennane, Salah, editor.
Ramos, Alexandre C. B., editor.
Series
Transactions on computer systems and networks.
Available in Other Form
Data Science.
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Online Access
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