Deep Learning : (Record no. 84443)
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fixed length control field | 08720nam a22011895i 4500 |
001 - CONTROL NUMBER | |
control field | 9783110670905 |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20240730161552.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 230228t20202020gw fo d z eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
ISBN | 9783110670905 |
041 0# - LANGUAGE CODE | |
Language code of text/sound track or separate title | |
245 00 - TITLE STATEMENT | |
Title | Deep Learning : |
Sub Title | Research and Applications / |
300 ## - PHYSICAL DESCRIPTION | |
Number of Pages | 1 online resource (IX, 152 p.) |
490 0# - SERIES STATEMENT | |
Series statement | De Gruyter Frontiers in Computational Intelligence , |
520 ## - SUMMARY, ETC. | |
Summary, etc | This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep learning is a new area of machine learning research which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non-immediately related fields, for example between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g. classification) and/or unsupervised (e.g. pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
General subdivision | Industrial applications. |
700 1# - AUTHOR 2 | |
Author 2 | Adate, Amit, |
700 1# - AUTHOR 2 | |
Author 2 | Arya, Dhruv, |
700 1# - AUTHOR 2 | |
Author 2 | Bhattacharyya, Siddhartha, |
700 1# - AUTHOR 2 | |
Author 2 | Bhattacharyya, Siddhartha, |
700 1# - AUTHOR 2 | |
Author 2 | Bose, Ankita, |
700 1# - AUTHOR 2 | |
Author 2 | Bose, Mahua, |
700 1# - AUTHOR 2 | |
Author 2 | Das, Swagatam, |
700 1# - AUTHOR 2 | |
Author 2 | Dey, Shatabhisa, |
700 1# - AUTHOR 2 | |
Author 2 | Ella Hassanien, Aboul, |
700 1# - AUTHOR 2 | |
Author 2 | Goswami, Soumyajit, |
700 1# - AUTHOR 2 | |
Author 2 | Jana, Ranjan, |
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Author 2 | Maheshwari, Karan, |
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Author 2 | Mali, Kalyani, |
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Author 2 | Mukherjee, Anirban, |
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Author 2 | Rajasekaran, Rajkumar, |
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Author 2 | Saha, Rajib, |
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Author 2 | Saha, Satadal, |
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Author 2 | Sarkar, Avik, |
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Author 2 | Shaha, Aditya, |
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Author 2 | Snasel, Vaclav, |
700 1# - AUTHOR 2 | |
Author 2 | Tripathy, B. K., |
700 1# - AUTHOR 2 | |
Author 2 | Tripathy, B. K., |
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Author 2 | Tripathy, B.K., |
856 40 - ELECTRONIC LOCATION AND ACCESS | |
Uniform Resource Identifier | https://doi.org/10.1515/9783110670905 |
856 40 - ELECTRONIC LOCATION AND ACCESS | |
Uniform Resource Identifier | https://www.degruyter.com/isbn/9783110670905 |
856 42 - ELECTRONIC LOCATION AND ACCESS | |
Uniform Resource Identifier | https://www.degruyter.com/document/cover/isbn/9783110670905/original |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Koha item type | eBooks |
264 #1 - | |
-- | Berlin ; |
-- | Boston : |
-- | De Gruyter, |
-- | [2020] |
264 #4 - | |
-- | ©2020 |
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-- | text |
-- | txt |
-- | rdacontent |
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-- | computer |
-- | c |
-- | rdamedia |
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-- | online resource |
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-- | text file |
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-- | Description based on online resource; title from PDF title page (publisher's Web site, viewed 28. Feb 2023) |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Artificial intelligence |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Machine learning. |
650 #4 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Algorithmus. |
650 #4 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Deep Learning. |
650 #4 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Maschinelles Lernen. |
650 #4 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Neuronales Netz. |
650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | COMPUTERS / Intelligence (AI) & Semantics. |
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-- | 978-3-11-065906-1 De Gruyter English eBooks 2020 - UC |
-- | 2020 |
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-- | 978-3-11-069627-1 DG Plus DeG Package 2020 Part 1 |
-- | 2020 |
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-- | 978-3-11-069628-8 DG Ebook Package English 2020 |
-- | 2020 |
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-- | 978-3-11-070471-6 EBOOK PACKAGE COMPLETE 2020 English |
-- | 2020 |
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-- | 978-3-11-070481-5 EBOOK PACKAGE Engineering, Computer Sciences 2020 English |
-- | 2020 |
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No items available.