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Q 23
Quantum machine learning / edited by Siddhartha Bhattacharyya, Indrajit Pan, Ashish Mani, Sourav De, Elizabeth Behrman, Susanta Chakraborti. - 1515/9783110670707. - Berlin ; Boston : De Gruyter, ©2020. - 1 online resource : il ( час. мин.), 1515/9783110670707. - (De Gruyter Frontiers in Computational Intelligence ; volume 6). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/3F9AE1C8-92D4-4D47-8B9B-3F82CBC46664. - ISBN 9783110670721. - ISBN 3110670720. - ISBN 3110670704 (electronic book). - ISBN 9783110670707 (electronic bk.)
Description based on online resource; title from PDF title page (viewed on September 10, 2020)
Параллельные издания:
1. Print version :
2. Print version :
~РУБ DDC 006.3/1
Рубрики: Machine learning.
Quantum theory.
Algorithmus
Künstliche Intelligenz
Maschinelles Lernen
Quantum Computing
COMPUTERS / Intelligence (AI) & Semantics
Аннотация: Quantum-enhanced machine learning refers to quantum algorithms that solve tasks in machine learning, thereby improving a classical machine learning method. Such algorithms typically require one to encode the given classical dataset into a quantum computer, so as to make it accessible for quantum information processing. After this, quantum information processing routines can be applied and the result of the quantum computation is read out by measuring the quantum system. While many proposals of quantum machine learning algorithms are still purely theoretical and require a full-scale universal quantum computer to be tested, others have been implemented on small-scale or special purpose quantum devices
Доп.точки доступа:
Bhattacharyya, Siddhartha, (1975-) \editor.\
Pan, Indrajit, (1983-) \editor.\
Mani, Ashish \ed.\
De, Sourav, (1979-) \editor.\
Behrman, Elizabeth \ed.\
Chakraborti, Susanta \ed.\
Q 23
Quantum machine learning / edited by Siddhartha Bhattacharyya, Indrajit Pan, Ashish Mani, Sourav De, Elizabeth Behrman, Susanta Chakraborti. - 1515/9783110670707. - Berlin ; Boston : De Gruyter, ©2020. - 1 online resource : il ( час. мин.), 1515/9783110670707. - (De Gruyter Frontiers in Computational Intelligence ; volume 6). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/3F9AE1C8-92D4-4D47-8B9B-3F82CBC46664. - ISBN 9783110670721. - ISBN 3110670720. - ISBN 3110670704 (electronic book). - ISBN 9783110670707 (electronic bk.)
Description based on online resource; title from PDF title page (viewed on September 10, 2020)
Параллельные издания:
1. Print version :
2. Print version :
Рубрики: Machine learning.
Quantum theory.
Algorithmus
Künstliche Intelligenz
Maschinelles Lernen
Quantum Computing
COMPUTERS / Intelligence (AI) & Semantics
Аннотация: Quantum-enhanced machine learning refers to quantum algorithms that solve tasks in machine learning, thereby improving a classical machine learning method. Such algorithms typically require one to encode the given classical dataset into a quantum computer, so as to make it accessible for quantum information processing. After this, quantum information processing routines can be applied and the result of the quantum computation is read out by measuring the quantum system. While many proposals of quantum machine learning algorithms are still purely theoretical and require a full-scale universal quantum computer to be tested, others have been implemented on small-scale or special purpose quantum devices
Доп.точки доступа:
Bhattacharyya, Siddhartha, (1975-) \editor.\
Pan, Indrajit, (1983-) \editor.\
Mani, Ashish \ed.\
De, Sourav, (1979-) \editor.\
Behrman, Elizabeth \ed.\
Chakraborti, Susanta \ed.\
2.
Подробнее
D 30
Deep Learning : : Research and Applications / / edited by Siddhartha Bhattacharyya, Vaclav Snasel, Aboul Ella Hassanien, Satadal Saha, B. K. Tripathy. - 1515/9783110670905. - Berlin ; ; Boston : : De Gruyter,, ©2020. - 1 online resource (IX, 152 p.). ( час. мин.), 1515/9783110670905. - (De Gruyter Frontiers in Computational Intelligence ; ; volume 7). - In English. - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/A6744D2D-9D62-4B48-93ED-B318614EC4E0. - ISBN 9783110670929 (electronic bk.). - ISBN 3110670925 (electronic bk.). - ISBN 9783110670905 (electronic book). - ISBN 3110670909 (electronic book)
Description based on online resource; title from PDF title page (publisher's Web site, viewed 23. Jun 2020).
Параллельные издания:
1.
2.
~РУБ DDC 006.3/1
Рубрики: Machine learning.
Artificial intelligence--Industrial applications.
Algorithmus.
Deep Learning.
Maschinelles Lernen.
Neuronales Netz.
COMPUTERS / Intelligence (AI) & Semantics.
Artificial intelligence--Industrial applications
Machine learning
Аннотация: 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.
Доп.точки доступа:
Bhattacharyya, Siddhartha, \ed.\
Ella Hassanien, Aboul, \ed.\
Saha, Satadal, \ed.\
Snasel, Vaclav, \ed.\
Tripathy, B. K., \ed.\
D 30
Deep Learning : : Research and Applications / / edited by Siddhartha Bhattacharyya, Vaclav Snasel, Aboul Ella Hassanien, Satadal Saha, B. K. Tripathy. - 1515/9783110670905. - Berlin ; ; Boston : : De Gruyter,, ©2020. - 1 online resource (IX, 152 p.). ( час. мин.), 1515/9783110670905. - (De Gruyter Frontiers in Computational Intelligence ; ; volume 7). - In English. - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/A6744D2D-9D62-4B48-93ED-B318614EC4E0. - ISBN 9783110670929 (electronic bk.). - ISBN 3110670925 (electronic bk.). - ISBN 9783110670905 (electronic book). - ISBN 3110670909 (electronic book)
Description based on online resource; title from PDF title page (publisher's Web site, viewed 23. Jun 2020).
Параллельные издания:
1.
2.
Рубрики: Machine learning.
Artificial intelligence--Industrial applications.
Algorithmus.
Deep Learning.
Maschinelles Lernen.
Neuronales Netz.
COMPUTERS / Intelligence (AI) & Semantics.
Artificial intelligence--Industrial applications
Machine learning
Аннотация: 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.
Доп.точки доступа:
Bhattacharyya, Siddhartha, \ed.\
Ella Hassanien, Aboul, \ed.\
Saha, Satadal, \ed.\
Snasel, Vaclav, \ed.\
Tripathy, B. K., \ed.\
3.
Подробнее
N 73
Noise filtering for big data analytics / / Souvik Bhattacharyya, Koushik Ghosh (eds.). - Berlin ; ; Boston: : De Gruyter,, [2022]. - 1 online resource : : il. - (De Gruyter series on the applications of mathematics in engineering and information sciences ; ; volume 12). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/A13D0852-2871-4AC1-8532-78CA054C6941. - ISBN 9783110697216 (electronic book). - ISBN 3110697211 (electronic book). - ISBN 9783110697261 (electronic publication). - ISBN 3110697262 (electronic publication)
Description based on online resource; title from digital title page (viewed on July 29, 2022).
Параллельные издания: Print version: : Noise filtering for big data analytics. - Berlin : De Gruyter, 2022. - ISBN 9783110697094
~РУБ DDC 005.7
Рубрики: Big data.
Data mining.
Information filtering systems.
Angewandte Mathematik.
Big Data.
Künstliche Intelligenz.
Maschinelles Lernen.
COMPUTERS / Information Technology.
Аннотация: This book explains how to perform data de-noising, in large scale, with a satisfactory level of accuracy. Three main issues are considered. Firstly, how to eliminate the error propagation from one stage to next stages while developing a filtered model. Secondly, how to maintain the positional importance of data whilst purifying it. Finally, preservation of memory in the data is crucial to extract smart data from noisy big data. If, after the application of any form of smoothing or filtering, the memory of the corresponding data changes heavily, then the final data may lose some important information. This may lead to wrong or erroneous conclusions. But, when anticipating any loss of information due to smoothing or filtering, one cannot avoid the process of denoising as on the other hand any kind of analysis of big data in the presence of noise can be misleading. So, the entire process demands very careful execution with efficient and smart models in order to effectively deal with it.
Доп.точки доступа:
Bhattacharyya, Souvik, \editor.\
Ghosh, Koushik, \editor.\
N 73
Noise filtering for big data analytics / / Souvik Bhattacharyya, Koushik Ghosh (eds.). - Berlin ; ; Boston: : De Gruyter,, [2022]. - 1 online resource : : il. - (De Gruyter series on the applications of mathematics in engineering and information sciences ; ; volume 12). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/A13D0852-2871-4AC1-8532-78CA054C6941. - ISBN 9783110697216 (electronic book). - ISBN 3110697211 (electronic book). - ISBN 9783110697261 (electronic publication). - ISBN 3110697262 (electronic publication)
Description based on online resource; title from digital title page (viewed on July 29, 2022).
Параллельные издания: Print version: : Noise filtering for big data analytics. - Berlin : De Gruyter, 2022. - ISBN 9783110697094
Рубрики: Big data.
Data mining.
Information filtering systems.
Angewandte Mathematik.
Big Data.
Künstliche Intelligenz.
Maschinelles Lernen.
COMPUTERS / Information Technology.
Аннотация: This book explains how to perform data de-noising, in large scale, with a satisfactory level of accuracy. Three main issues are considered. Firstly, how to eliminate the error propagation from one stage to next stages while developing a filtered model. Secondly, how to maintain the positional importance of data whilst purifying it. Finally, preservation of memory in the data is crucial to extract smart data from noisy big data. If, after the application of any form of smoothing or filtering, the memory of the corresponding data changes heavily, then the final data may lose some important information. This may lead to wrong or erroneous conclusions. But, when anticipating any loss of information due to smoothing or filtering, one cannot avoid the process of denoising as on the other hand any kind of analysis of big data in the presence of noise can be misleading. So, the entire process demands very careful execution with efficient and smart models in order to effectively deal with it.
Доп.точки доступа:
Bhattacharyya, Souvik, \editor.\
Ghosh, Koushik, \editor.\
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