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Page 3, Results: 24
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D 30
Deep learning : : concepts, methodologies, tools, and applications / / Information Resources Management Association. - Hershey, PA : : Engineering Science Reference, an imprint of IGI Global,, [2020]. - 1 online resource. - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/4F164494-6337-4040-89FD-4414AC129503. - ISBN 1799804151 (electronic book). - ISBN 9781799804161 (electronic book). - ISBN 179980416X (electronic book). - ISBN 9781799804154 (electronic bk.)
Description based on online resource; title from digital title page (viewed on November 26, 2019).
Параллельные издания: Print version: : Deep learning (Engineering Science Reference (Publisher)) Deep learning. - Hershey : Engineering Science Reference, 2019. - ISBN 9781799804147
~РУБ DDC 006.3/12
Рубрики: Data mining.
Machine learning--Industrial applications.
Big data.
Invisible Web.
Big data.
Data mining.
Invisible Web.
Machine learning--Industrial applications.
Аннотация: "This book is a vital reference source that trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. It also explores the latest concepts, algorithms, and techniques of deep learning and data mining and analysis"--
Доп.точки доступа:
Information Resources Management Association,
D 30
Deep learning : : concepts, methodologies, tools, and applications / / Information Resources Management Association. - Hershey, PA : : Engineering Science Reference, an imprint of IGI Global,, [2020]. - 1 online resource. - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/4F164494-6337-4040-89FD-4414AC129503. - ISBN 1799804151 (electronic book). - ISBN 9781799804161 (electronic book). - ISBN 179980416X (electronic book). - ISBN 9781799804154 (electronic bk.)
Description based on online resource; title from digital title page (viewed on November 26, 2019).
Параллельные издания: Print version: : Deep learning (Engineering Science Reference (Publisher)) Deep learning. - Hershey : Engineering Science Reference, 2019. - ISBN 9781799804147
Рубрики: Data mining.
Machine learning--Industrial applications.
Big data.
Invisible Web.
Big data.
Data mining.
Invisible Web.
Machine learning--Industrial applications.
Аннотация: "This book is a vital reference source that trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. It also explores the latest concepts, algorithms, and techniques of deep learning and data mining and analysis"--
Доп.точки доступа:
Information Resources Management Association,
22.
Подробнее
D 30
Deep learning techniques and optimization strategies in big data analytics / / [edited by] J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Pandian Vasant. - Hershey, PA : : Engineering Science Reference, IGI Global,, [2020]. - 1 online resource (xxv, 355 pages). : il. - (Advances in Systems Analysis, Software Engineering, and High Performance Computing (ASASEHPC) Book Series). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/449CF960-666A-4A3E-B942-6F8D52B33666. - ISBN 1799811948 (electronic book). - ISBN 9781799811947 (electronic bk.)
Description based on online resource; title from digital title page (viewed on December 02, 2019).
Параллельные издания: Print version: : Deep learning techniques and optimization strategies in big data analytics. - Hershey, PA : Engineering Science Reference, [2020]. - ISBN 9781799811923
~РУБ DDC 005.7
Рубрики: Big data.
Quantitative research.
Big data.
Quantitative research.
Аннотация: "This book examines the application of artificial intelligence in machine learning, data mining in unstructured data sets or databases, web mining, and information retrieval"--
Доп.точки доступа:
Thomas, J. Joshua, (1973-) \editor.\
D 30
Deep learning techniques and optimization strategies in big data analytics / / [edited by] J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Pandian Vasant. - Hershey, PA : : Engineering Science Reference, IGI Global,, [2020]. - 1 online resource (xxv, 355 pages). : il. - (Advances in Systems Analysis, Software Engineering, and High Performance Computing (ASASEHPC) Book Series). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/449CF960-666A-4A3E-B942-6F8D52B33666. - ISBN 1799811948 (electronic book). - ISBN 9781799811947 (electronic bk.)
Description based on online resource; title from digital title page (viewed on December 02, 2019).
Параллельные издания: Print version: : Deep learning techniques and optimization strategies in big data analytics. - Hershey, PA : Engineering Science Reference, [2020]. - ISBN 9781799811923
Рубрики: Big data.
Quantitative research.
Big data.
Quantitative research.
Аннотация: "This book examines the application of artificial intelligence in machine learning, data mining in unstructured data sets or databases, web mining, and information retrieval"--
Доп.точки доступа:
Thomas, J. Joshua, (1973-) \editor.\
23.
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B 57
Big data analytics for improved accuracy, efficiency, and decision making in digital marketing / / Amandeep Singh, editor. - Hershey, PA : : Business Science Reference,, 2021. - 1 online resource. - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/5F6080E4-37AB-4ABF-A3DA-63245E7DA251. - ISBN 9781799872337 (ebook). - ISBN 1799872335. - ISBN 9781799872344 (electronic bk.). - ISBN 1799872343 (electronic bk.)
Description based on print version record and CIP data provided by publisher; resource not viewed.
Параллельные издания: Print version: : Big data analytics for improved accuracy, efficiency, and decision making in digital marketing. - Hershey, PA : Business Science Reference, 2021. - ISBN 9781799872313
~РУБ DDC 658.8/72
Рубрики: Internet marketing--Statistical methods.
Decision making--Statistical methods.
Big data.
Аннотация: "This book will help the digital marketers to understand the Big Data in a better way and to get a meaning full information, which, will help the marketers to better market their products to the consumers"--
Доп.точки доступа:
Singh, Amandeep, (1982-) \editor.\
B 57
Big data analytics for improved accuracy, efficiency, and decision making in digital marketing / / Amandeep Singh, editor. - Hershey, PA : : Business Science Reference,, 2021. - 1 online resource. - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/5F6080E4-37AB-4ABF-A3DA-63245E7DA251. - ISBN 9781799872337 (ebook). - ISBN 1799872335. - ISBN 9781799872344 (electronic bk.). - ISBN 1799872343 (electronic bk.)
Description based on print version record and CIP data provided by publisher; resource not viewed.
Параллельные издания: Print version: : Big data analytics for improved accuracy, efficiency, and decision making in digital marketing. - Hershey, PA : Business Science Reference, 2021. - ISBN 9781799872313
Рубрики: Internet marketing--Statistical methods.
Decision making--Statistical methods.
Big data.
Аннотация: "This book will help the digital marketers to understand the Big Data in a better way and to get a meaning full information, which, will help the marketers to better market their products to the consumers"--
Доп.точки доступа:
Singh, Amandeep, (1982-) \editor.\
24.
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G 42
Ghavami, Peter,.
Big Data Analytics Methods : : Analytics Techniques in Data Mining, Deep Learning and Natural Language Processing / / Peter Ghavami. - 2nd Edition. - 1515/9781547401567. - Berlin ; ; Boston : : De Gruyter,, ©2020. - 1 online resource (XVI, 238 pages) : гербы ( час. мин.), 1515/9781547401567. - In English. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/00EA0348-14BD-4E27-9B5B-C44E78DA8323. - ISBN 9781547401567. - ISBN 1547401567. - ISBN 9781547401581. - ISBN 1547401583. - ISBN 9781547417957. - ISBN 1547417951
Description based on online resource; title from PDF title page (publisher's Web site, viewed 21. Dez 2019).
Параллельные издания:
1.
2.
~РУБ DDC 005.7
Рубрики: Big data.
Data analysis.
Data mining.
Machine learning.
Neural networks.
BUSINESS & ECONOMICS / Information Management.
Big data.
Data mining.
Natural language processing (Computer science)
Big data.
Data mining.
Natural language processing (Computer science)
Аннотация: Big Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensembles of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods provide big data professionals, business intelligence professionals and citizen data scientists insight on how to overcome challenges and avoid common pitfalls and traps in data analytics. The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. It discusses data visualization, prediction, optimization, artificial intelligence, regression analysis, the Cox hazard model and many analytics using case examples with applications in the healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services industries. This book's state of the art treatment of advanced data analytics methods and important best practices will help readers succeed in data analytics.
G 42
Ghavami, Peter,.
Big Data Analytics Methods : : Analytics Techniques in Data Mining, Deep Learning and Natural Language Processing / / Peter Ghavami. - 2nd Edition. - 1515/9781547401567. - Berlin ; ; Boston : : De Gruyter,, ©2020. - 1 online resource (XVI, 238 pages) : гербы ( час. мин.), 1515/9781547401567. - In English. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/00EA0348-14BD-4E27-9B5B-C44E78DA8323. - ISBN 9781547401567. - ISBN 1547401567. - ISBN 9781547401581. - ISBN 1547401583. - ISBN 9781547417957. - ISBN 1547417951
Description based on online resource; title from PDF title page (publisher's Web site, viewed 21. Dez 2019).
Параллельные издания:
1.
2.
Рубрики: Big data.
Data analysis.
Data mining.
Machine learning.
Neural networks.
BUSINESS & ECONOMICS / Information Management.
Big data.
Data mining.
Natural language processing (Computer science)
Big data.
Data mining.
Natural language processing (Computer science)
Аннотация: Big Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensembles of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods provide big data professionals, business intelligence professionals and citizen data scientists insight on how to overcome challenges and avoid common pitfalls and traps in data analytics. The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. It discusses data visualization, prediction, optimization, artificial intelligence, regression analysis, the Cox hazard model and many analytics using case examples with applications in the healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services industries. This book's state of the art treatment of advanced data analytics methods and important best practices will help readers succeed in data analytics.
Page 3, Results: 24