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| LEADER |
02338nam a2200385 i 4500 |
| 001 |
181-020295851 |
| 003 |
Uk |
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20210927132024.0 |
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200729s2021 si a b 001 0 eng |
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tu |
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|a 2020034548
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|q (hardcover)
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|a DLC
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|b G44 2021
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0 |
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|a 006.3/1
|2 23
|
| 245 |
0 |
0 |
|a Generalization with deep learning
|b for improvement on sensing capability
|c editors, Zhenghua Chen, Min Wu, Xiaoli Li, Institute for Infocomm Research, Singapore
|
| 264 |
|
1 |
|a Singapore ;
|a Hackensack, NJ :
|b World Scientific
|c [2021]
|
| 300 |
|
|
|a ix, 314 pages :
|b illustrations ;
|c 25 cm
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a unmediated
|b n
|2 rdamedia
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| 338 |
|
|
|a volume
|b nc
|2 rdacarrier
|
| 504 |
|
|
|a Includes bibliographical references and index.
|
| 520 |
|
|
|a "Deep Learning has achieved great success in many challenging research areas, such as image recognition and natural language processing. The key merit of deep learning is to automatically learn good feature representation from massive data conceptually. In this book, we will show that the deep learning technology can be a very good candidate for improving sensing capabilities. In this edited volume, we aim to narrow the gap between human and machine by showcasing various deep learning applications in the area of sensing. The book will cover the fundamentals of deep learning techniques and their applications in real-world problems including activity sensing, remote sensing and medical sensing. It will demonstrate how different deep learning techniques help to improve the sensing capabilities and enable scientists and practitioners to make insightful observations and generate invaluable discoveries from different types of data"--
|c Provided by publisher.
|
| 650 |
|
0 |
|a Electronic surveillance
|x Data processing.
|
| 650 |
|
0 |
|a Remote sensing
|x Data processing.
|
| 650 |
|
0 |
|a Diagnostic imaging
|x Data processing.
|
| 650 |
|
0 |
|a Machine learning.
|
| 700 |
1 |
|
|a Chen, Zhenghua
|e editor.
|
| 700 |
1 |
|
|a Wu, Min
|d 1974-
|e editor.
|
| 700 |
1 |
|
|a Li, Xiao-Li
|d 1969-
|e editor.
|
| 852 |
1 |
1 |
|a British Library
|b STI
|k (B)
|h 006.31
|
| 980 |
|
|
|a 020295851
|b 181
|c sid-181-col-blfidbbi
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SOLR
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1778756078870200320 |
| access_facet |
Local Holdings |
| author2 |
Chen, Zhenghua, Wu, Min, Li, Xiao-Li |
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edt, edt, edt |
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z c zc, m w mw, x l l xll |
| author_facet |
Chen, Zhenghua, Wu, Min, Li, Xiao-Li |
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Library A |
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T - Technology |
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TK7882 |
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TK7882.E2 G44 2021 |
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TK7882.E2 G44 2021 |
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TK 47882 E2 G44 42021 |
| callnumber-subject |
TK - Electrical and Nuclear Engineering |
| collection |
sid-181-col-blfidbbi |
| contents |
"Deep Learning has achieved great success in many challenging research areas, such as image recognition and natural language processing. The key merit of deep learning is to automatically learn good feature representation from massive data conceptually. In this book, we will show that the deep learning technology can be a very good candidate for improving sensing capabilities. In this edited volume, we aim to narrow the gap between human and machine by showcasing various deep learning applications in the area of sensing. The book will cover the fundamentals of deep learning techniques and their applications in real-world problems including activity sensing, remote sensing and medical sensing. It will demonstrate how different deep learning techniques help to improve the sensing capabilities and enable scientists and practitioners to make insightful observations and generate invaluable discoveries from different types of data"-- |
| dewey-full |
006.3/1 |
| dewey-hundreds |
000 - Computer science, information & general works |
| dewey-ones |
006 - Special computer methods |
| dewey-raw |
006.3/1 |
| dewey-search |
006.3/1 |
| dewey-sort |
16.3 11 |
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000 - Computer science, knowledge & systems |
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Informatik, Technik |
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science-computerscience |
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Book |
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Book, E-Book |
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Book, E-Book |
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Book, E-Book |
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Buch |
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text-print-monograph-independent |
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e-Book |
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Book, E-Book |
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Book |
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Book, E-Book |
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Book, E-Book |
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Book |
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not assigned |
| geogr_code_person |
not assigned |
| id |
181-020295851 |
| illustrated |
Illustrated |
| imprint |
Singapore ;, Hackensack, NJ :, World Scientific, [2021] |
| imprint_str_mv |
Singapore ; Hackensack, NJ : World Scientific [2021] |
| institution |
FID-BBI-DE-23 |
| is_hierarchy_id |
|
| is_hierarchy_title |
|
| isbn |
9789811218835 |
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9789811218842, 9789811218859 |
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FID-BBI-DE-23 |
| language |
English |
| last_indexed |
2023-10-03T17:26:26.84Z |
| lccn |
2020034548 |
| match_str |
chen2021generalizationwithdeeplearningforimprovementonsensingcapability |
| mega_collection |
British Library Catalogue |
| physical |
ix, 314 pages; illustrations; 25 cm |
| publishDate |
[2021] |
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2021 |
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Singapore ; |
| publisher |
World Scientific |
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marcfinc |
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020295851 |
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marcfinc |
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No subject assigned |
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181 |
| spelling |
Generalization with deep learning for improvement on sensing capability editors, Zhenghua Chen, Min Wu, Xiaoli Li, Institute for Infocomm Research, Singapore, Singapore ; Hackensack, NJ : World Scientific [2021], ix, 314 pages : illustrations ; 25 cm, text txt rdacontent, unmediated n rdamedia, volume nc rdacarrier, Includes bibliographical references and index., "Deep Learning has achieved great success in many challenging research areas, such as image recognition and natural language processing. The key merit of deep learning is to automatically learn good feature representation from massive data conceptually. In this book, we will show that the deep learning technology can be a very good candidate for improving sensing capabilities. In this edited volume, we aim to narrow the gap between human and machine by showcasing various deep learning applications in the area of sensing. The book will cover the fundamentals of deep learning techniques and their applications in real-world problems including activity sensing, remote sensing and medical sensing. It will demonstrate how different deep learning techniques help to improve the sensing capabilities and enable scientists and practitioners to make insightful observations and generate invaluable discoveries from different types of data"-- Provided by publisher., Electronic surveillance Data processing., Remote sensing Data processing., Diagnostic imaging Data processing., Machine learning., Chen, Zhenghua editor., Wu, Min 1974- editor., Li, Xiao-Li 1969- editor., British Library STI (B) 006.31 |
| spellingShingle |
Generalization with deep learning: for improvement on sensing capability, "Deep Learning has achieved great success in many challenging research areas, such as image recognition and natural language processing. The key merit of deep learning is to automatically learn good feature representation from massive data conceptually. In this book, we will show that the deep learning technology can be a very good candidate for improving sensing capabilities. In this edited volume, we aim to narrow the gap between human and machine by showcasing various deep learning applications in the area of sensing. The book will cover the fundamentals of deep learning techniques and their applications in real-world problems including activity sensing, remote sensing and medical sensing. It will demonstrate how different deep learning techniques help to improve the sensing capabilities and enable scientists and practitioners to make insightful observations and generate invaluable discoveries from different types of data"--, Electronic surveillance Data processing., Remote sensing Data processing., Diagnostic imaging Data processing., Machine learning. |
| title |
Generalization with deep learning: for improvement on sensing capability |
| title_auth |
Generalization with deep learning for improvement on sensing capability |
| title_full |
Generalization with deep learning for improvement on sensing capability editors, Zhenghua Chen, Min Wu, Xiaoli Li, Institute for Infocomm Research, Singapore |
| title_fullStr |
Generalization with deep learning for improvement on sensing capability editors, Zhenghua Chen, Min Wu, Xiaoli Li, Institute for Infocomm Research, Singapore |
| title_full_unstemmed |
Generalization with deep learning for improvement on sensing capability editors, Zhenghua Chen, Min Wu, Xiaoli Li, Institute for Infocomm Research, Singapore |
| title_short |
Generalization with deep learning |
| title_sort |
generalization with deep learning for improvement on sensing capability |
| title_sub |
for improvement on sensing capability |
| topic |
Electronic surveillance Data processing., Remote sensing Data processing., Diagnostic imaging Data processing., Machine learning. |
| topic_facet |
Electronic surveillance, Remote sensing, Diagnostic imaging, Machine learning., Data processing. |