Advances in Deep Learning (1st ed. 2020) (Studies in Big Data #57)
By: and and and
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- Synopsis
- This book introduces readers to both basic and advanced concepts in deep network models. It covers state-of-the-art deep architectures that many researchers are currently using to overcome the limitations of the traditional artificial neural networks. Various deep architecture models and their components are discussed in detail, and subsequently illustrated by algorithms and selected applications. In addition, the book explains in detail the transfer learning approach for faster training of deep models; the approach is also demonstrated on large volumes of fingerprint and face image datasets. In closing, it discusses the unique set of problems and challenges associated with these models.
- Copyright:
- 2020
Book Details
- Book Quality:
- Publisher Quality
- ISBN-13:
- 9789811367946
- Related ISBNs:
- 9789811367939
- Publisher:
- Springer Singapore, Singapore
- Date of Addition:
- 03/21/19
- Copyrighted By:
- Springer Nature Singapore Pte Ltd.
- Adult content:
- No
- Language:
- English
- Has Image Descriptions:
- No
- Categories:
- Nonfiction, Computers and Internet, Mathematics and Statistics
- Submitted By:
- Bookshare Staff
- Usage Restrictions:
- This is a copyrighted book.
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