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| LEADER |
00000uam a2200000 a 4500 |
| 001 |
in00004100308 |
| 005 |
20260128222004.6 |
| 006 |
m o d |
| 007 |
cr cn |
| 008 |
190919s2019 xx o eng |
| 020 |
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|z 9780128136607
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| 020 |
|
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|z 9780128136591
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| 035 |
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|a (CaSebORM)9780128136607
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| 040 |
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|d UtOrBLW
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| 041 |
0 |
|
|a eng
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| 100 |
1 |
|
|a Wang, Zhangyang,
|e author.
|0 http://id.loc.gov/authorities/names/no2016146507
|
| 245 |
1 |
0 |
|a Deep Learning through Sparse and Low-Rank Modeling /
|c Wang, Zhangyang.
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| 250 |
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|a 1st edition.
|
| 264 |
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1 |
|b Academic Press,
|c 2019.
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| 300 |
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|a 1 online resource (296 pages)
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
|
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 347 |
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|a text file
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| 520 |
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|a Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models-those that emphasize problem-specific Interpretability-with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics. Combines classical sparse and low-rank models and algorithms with the latest advances in deep learning networks Shows how the structure and algorithms of sparse and low-rank methods improves the performance and interpretability of Deep Learning models Provides tactics on how to build and apply customized deep learning models for various applications
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| 533 |
|
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|a Electronic reproduction.
|b Boston, MA :
|c Safari,
|n Available via World Wide Web.
|d 2019.
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| 538 |
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|a Mode of access: World Wide Web.
|
| 542 |
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|f Copyright Elsevier Science & Technology
|g 2019
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| 588 |
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|a Online resource; Title from title page (viewed April 11, 2019)
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| 500 |
|
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|a Electronic resource.
|
| 655 |
|
7 |
|a Electronic books.
|2 local
|
| 700 |
1 |
|
|a Fu, Yun,
|e author.
|0 http://id.loc.gov/authorities/names/no2005113406
|
| 700 |
1 |
|
|a Huang, Thomas,
|e author.
|
| 710 |
2 |
|
|a Safari, an O'Reilly Media Company.
|
| 856 |
4 |
0 |
|u https://proxy.library.tamu.edu/login?url=https://go.oreilly.com/TAMU/library/view/-/9780128136607/?ar
|z Connect to this electronic resource
|t 0
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| 999 |
f |
f |
|s 5e12142d-fa94-3495-a84e-358f1fcf3c95
|i d625a929-5587-3562-bd90-d137ba243dce
|t 0
|
| 952 |
f |
f |
|a Texas A&M University
|b College Station
|c Electronic Resources
|s www_evans
|d Available Online
|t 0
|h No information provided
|
| 998 |
f |
f |
|t 0
|l Available Online
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