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C 43
Challenges and applications for implementing machine learning in computer vision / / Ramgopal Kashyap, Amity University, Raipur, India, A.V. Senthil Kumar, Hindusthan College of arts and science, India. - 4018/978-1-7998-0182-5. - Hershey, PA : : IGI Global/Engineering Science Reference, an imprint of IGI Global,, [2020]. - 1 online resource (xxv, 293 pages). : il ( час. мин.), 4018/978-1-7998-0182-5. - (IGI global disseminator of knowledge). - Includes bibliographical references (pages 271-287) and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/47EE98A1-4E20-45B4-AE52-9D8F6A43D5BE. - ISBN 9781799801856 (ebook). - ISBN 1799801853. - ISBN 1799801845 (electronic book). - ISBN 9781799801849 (electronic bk.)
Description based on print version record and CIP data provided by publisher; resource not viewed.
Параллельные издания: Print version: : Challenges and applications for implementing machine learning in computer vision. - Hershey, PA : IGI Global/Engineering Science Reference, an imprint of IGI Global, [2020]. - ISBN 9781799801825
Содержание:
Chapter 1. Development of class attendance system using face recognition for faculty of mechanical and manufacturing engineering, Universiti Tun Hussein Onn Malaysia -- Chapter 2. Deep learning in computational neuroscience -- Chapter 3. Advanced diagnosis techniques in medical imaging -- Chapter 4. Challenges of applying deep learning in real-world applications -- Chapter 5. Challenges and applications for implementing machine learning in computer vision: machine learning applications and approaches -- Chapter 6. Medical imaging importance in the real world -- Chapter 7. Image processing approaches and disaster management -- Chapter 8. Artificial intelligence and machine learning algorithms -- Chapter 9. Application of content-based image retrieval in medical image acquisition -- Chapter 10. Machine learning for health data analytics: a few case studies of application of regression.
~РУБ DDC 006.3/7
Рубрики: Computer vision.
Machine learning.
Computer vision.
Machine learning.
Аннотация: "This book examines the latest advances and trends in computer vision and machine learning algorithms for various applications"--
Доп.точки доступа:
Kashyap, Ramgopal, (1984-) \editor.\
Kumar, A. V. Senthil, (1966-) \editor.\
C 43
Challenges and applications for implementing machine learning in computer vision / / Ramgopal Kashyap, Amity University, Raipur, India, A.V. Senthil Kumar, Hindusthan College of arts and science, India. - 4018/978-1-7998-0182-5. - Hershey, PA : : IGI Global/Engineering Science Reference, an imprint of IGI Global,, [2020]. - 1 online resource (xxv, 293 pages). : il ( час. мин.), 4018/978-1-7998-0182-5. - (IGI global disseminator of knowledge). - Includes bibliographical references (pages 271-287) and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/47EE98A1-4E20-45B4-AE52-9D8F6A43D5BE. - ISBN 9781799801856 (ebook). - ISBN 1799801853. - ISBN 1799801845 (electronic book). - ISBN 9781799801849 (electronic bk.)
Description based on print version record and CIP data provided by publisher; resource not viewed.
Параллельные издания: Print version: : Challenges and applications for implementing machine learning in computer vision. - Hershey, PA : IGI Global/Engineering Science Reference, an imprint of IGI Global, [2020]. - ISBN 9781799801825
Содержание:
Chapter 1. Development of class attendance system using face recognition for faculty of mechanical and manufacturing engineering, Universiti Tun Hussein Onn Malaysia -- Chapter 2. Deep learning in computational neuroscience -- Chapter 3. Advanced diagnosis techniques in medical imaging -- Chapter 4. Challenges of applying deep learning in real-world applications -- Chapter 5. Challenges and applications for implementing machine learning in computer vision: machine learning applications and approaches -- Chapter 6. Medical imaging importance in the real world -- Chapter 7. Image processing approaches and disaster management -- Chapter 8. Artificial intelligence and machine learning algorithms -- Chapter 9. Application of content-based image retrieval in medical image acquisition -- Chapter 10. Machine learning for health data analytics: a few case studies of application of regression.
Рубрики: Computer vision.
Machine learning.
Computer vision.
Machine learning.
Аннотация: "This book examines the latest advances and trends in computer vision and machine learning algorithms for various applications"--
Доп.точки доступа:
Kashyap, Ramgopal, (1984-) \editor.\
Kumar, A. V. Senthil, (1966-) \editor.\
52.
Подробнее
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/43121066-BC0F-4D45-885E-683DB62AC286. - 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/43121066-BC0F-4D45-885E-683DB62AC286. - 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.
53.
Подробнее
M 13
Machine learning and data analytics for predicting, managing, and monitoring disease / / Manikant Roy and Lovi Raj Gupta, editors. - Hershey : : Engineering Science Reference,, [2022]. - 1 online resource. - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/2D163384-8F03-40E6-861B-F695DB85AB12. - ISBN 9781799871903 (ebook). - ISBN 1799871908 (ebook). - ISBN 9781799871910 (electronic bk.). - ISBN 1799871916 (electronic bk.)
Print version record and CIP data provided by publisher; resource not viewed.
Параллельные издания: Print version: : Machine learning and data analytics for predicting, managing, and monitoring disease. - Hershey : Engineering Science Reference, [2022]. - ISBN 9781799871880
~РУБ DDC 614.40285
Рубрики: Epidemiology--Data processing.
Epidemics--Mathematical models.
Machine learning.
Artificial intelligence--Medical applications.
Epidemiologic Methods
Artificial intelligence--Medical applications.
Epidemics--Mathematical models.
Epidemiology--Data processing.
Machine learning.
Аннотация: "This book provides the recent various theoretical frameworks, empirical research and application of advanced analytics methods for disease detection, pandemic management, disease prediction etc. using the data analysis methods and their usages for taking timely decisions for prevention of such spread of pandemic and how people in government, society and administer can use these insights for overall management"--
Доп.точки доступа:
Roy, Manikant, (1989-) \editor.\
Gupta, Lovi Raj, \editor.\
M 13
Machine learning and data analytics for predicting, managing, and monitoring disease / / Manikant Roy and Lovi Raj Gupta, editors. - Hershey : : Engineering Science Reference,, [2022]. - 1 online resource. - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/2D163384-8F03-40E6-861B-F695DB85AB12. - ISBN 9781799871903 (ebook). - ISBN 1799871908 (ebook). - ISBN 9781799871910 (electronic bk.). - ISBN 1799871916 (electronic bk.)
Print version record and CIP data provided by publisher; resource not viewed.
Параллельные издания: Print version: : Machine learning and data analytics for predicting, managing, and monitoring disease. - Hershey : Engineering Science Reference, [2022]. - ISBN 9781799871880
Рубрики: Epidemiology--Data processing.
Epidemics--Mathematical models.
Machine learning.
Artificial intelligence--Medical applications.
Epidemiologic Methods
Artificial intelligence--Medical applications.
Epidemics--Mathematical models.
Epidemiology--Data processing.
Machine learning.
Аннотация: "This book provides the recent various theoretical frameworks, empirical research and application of advanced analytics methods for disease detection, pandemic management, disease prediction etc. using the data analysis methods and their usages for taking timely decisions for prevention of such spread of pandemic and how people in government, society and administer can use these insights for overall management"--
Доп.точки доступа:
Roy, Manikant, (1989-) \editor.\
Gupta, Lovi Raj, \editor.\
54.
Подробнее
D 30
Deep neural networks for multimodal imaging and biomedical applications / / Annamalai Suresh, R. Udendran, S. Vimal. - 4018/978-1-7998-3591-2. - Hershey, PA : : Medical Information Science Reference, an imprint of IGI Global,, [2020]. - 1 online resource (xvi, 294 pages) : : il ( час. мин.), 4018/978-1-7998-3591-2. - (Advances in bioinformatics and biomedical engineering (ABBE) book series). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/36D920A4-1DD0-4BE6-889A-FE7B2FAC7F16. - ISBN 9781799835929 (electronic book). - ISBN 1799835928 (electronic book). - ISBN 1799835936. - ISBN 9781799835936 (electronic bk.)
"Premier Reference Source" -- taken from front cover. Description based on online resource; title from digital title page (viewed on August 06, 2020).
Параллельные издания: Print version: : Deep neural networks for multimodal imaging and biomedical applications. - Hershey, PA : Medical Information Science Reference, [2020]. - ISBN 9781799835912
Содержание:
The Pivotal Role of Edge Computing With Machine Learning And It's Impact On Healthcare / Muthukumari S.M., George Dharma Prakash Raj -- Exploring Internet of Things and Artificial Intelligence for smart Healthcare solutions / G. Yamini Yamini -- A Comparative Study of Popular CNN Topologies Used For Imagenet Classification / Hmidi Alaeddine, Malek Jihene -- Advancements in Techniques of Biomedical Image Analysis / Rajitha B. -- Demystification of Deep Learning-driven Medical Image Processing and its impact on future Biomedical Applications / Udendhran Mudaliyar, M. Bala Murugan, Suresh Annamalai -- Transforming Biomedical Applications through Smart Sensing and Artificial Intelligence / Harini T.J., Suresh V., Carmel M. -- Use of eggshell as partial replacement for sand in concrete used in biomedical applications / Sebastin S., Murali Ram Kumar S.M. -- Deep Learning Models for Semantic Multi modal Medical Image Segmentation / V.R.S. Mani.
~РУБ DDC 610.285
Рубрики: Machine learning.
Computational intelligence.
Artificial intelligence--Medical applications.
Deep Learning
Multimodal Imaging--methods
Image Interpretation, Computer-Assisted--methods
Biomedical Technology--methods
Apprentissage automatique.
Intelligence informatique.
Intelligence artificielle en médecine.
Apprentissage profond.
Artificial intelligence--Medical applications
Computational intelligence
Machine learning
Аннотация: "This book provides research exploring the theoretical and practical aspects of emerging data computing methods and imaging techniques within healthcare and biomedicine. The publication provides a complete set of information in a single module starting from developing deep neural networks to predicting disease by employing multi-modal imaging"--
Доп.точки доступа:
Suresh, Annamalai, (1977-) \editor.\
Udendran, R., (1992-) \editor.\
Vimal, S., (1984-) \editor.\
D 30
Deep neural networks for multimodal imaging and biomedical applications / / Annamalai Suresh, R. Udendran, S. Vimal. - 4018/978-1-7998-3591-2. - Hershey, PA : : Medical Information Science Reference, an imprint of IGI Global,, [2020]. - 1 online resource (xvi, 294 pages) : : il ( час. мин.), 4018/978-1-7998-3591-2. - (Advances in bioinformatics and biomedical engineering (ABBE) book series). - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/36D920A4-1DD0-4BE6-889A-FE7B2FAC7F16. - ISBN 9781799835929 (electronic book). - ISBN 1799835928 (electronic book). - ISBN 1799835936. - ISBN 9781799835936 (electronic bk.)
"Premier Reference Source" -- taken from front cover. Description based on online resource; title from digital title page (viewed on August 06, 2020).
Параллельные издания: Print version: : Deep neural networks for multimodal imaging and biomedical applications. - Hershey, PA : Medical Information Science Reference, [2020]. - ISBN 9781799835912
Содержание:
The Pivotal Role of Edge Computing With Machine Learning And It's Impact On Healthcare / Muthukumari S.M., George Dharma Prakash Raj -- Exploring Internet of Things and Artificial Intelligence for smart Healthcare solutions / G. Yamini Yamini -- A Comparative Study of Popular CNN Topologies Used For Imagenet Classification / Hmidi Alaeddine, Malek Jihene -- Advancements in Techniques of Biomedical Image Analysis / Rajitha B. -- Demystification of Deep Learning-driven Medical Image Processing and its impact on future Biomedical Applications / Udendhran Mudaliyar, M. Bala Murugan, Suresh Annamalai -- Transforming Biomedical Applications through Smart Sensing and Artificial Intelligence / Harini T.J., Suresh V., Carmel M. -- Use of eggshell as partial replacement for sand in concrete used in biomedical applications / Sebastin S., Murali Ram Kumar S.M. -- Deep Learning Models for Semantic Multi modal Medical Image Segmentation / V.R.S. Mani.
Рубрики: Machine learning.
Computational intelligence.
Artificial intelligence--Medical applications.
Deep Learning
Multimodal Imaging--methods
Image Interpretation, Computer-Assisted--methods
Biomedical Technology--methods
Apprentissage automatique.
Intelligence informatique.
Intelligence artificielle en médecine.
Apprentissage profond.
Artificial intelligence--Medical applications
Computational intelligence
Machine learning
Аннотация: "This book provides research exploring the theoretical and practical aspects of emerging data computing methods and imaging techniques within healthcare and biomedicine. The publication provides a complete set of information in a single module starting from developing deep neural networks to predicting disease by employing multi-modal imaging"--
Доп.точки доступа:
Suresh, Annamalai, (1977-) \editor.\
Udendran, R., (1992-) \editor.\
Vimal, S., (1984-) \editor.\
55.
Подробнее
K 20
Kapoor, Amita,.
Deep learning with TensorFlow and Keras [[electronic resource] :] : build and deploy supervised,... unsupervised, deep, and reinforcement learning mod. / Amita, Kapoor ; author.: Gulli, Antonio,, Pal, Sujit ; writer of foreword. Chollet, François,. - Third edition. - [Б. м.] : PACKT PUBLISHING LIMITED,, 2022. - 1 online resource (698 pages) : : il. - (Expert insight). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/1FBB3E36-4936-46C3-A98B-56628384A89C. - ISBN 9781803245713 (electronic bk.). - ISBN 1803245719 (electronic bk.)
Параллельные издания: Print version: :
~РУБ DDC 005.13/3
Рубрики: Machine learning.
Artificial intelligence.
Neural networks (Computer science)
Python (Computer program language)
Аннотация: Deep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow 2.x focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs based on Keras, and flexible model building on any platform. This book uses the latest TF 2.0 features and libraries to present an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical examples for the cloud, mobile, and large production environments. This book also shows you how to create neural networks with TensorFlow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), recurrent neural networks (RNNs), natural language processing (NLP), and graph neural networks (GNNs)), covers working example apps, and then dives into TF in production, TF mobile, and TensorFlow with AutoML.
Доп.точки доступа:
Gulli, Antonio, \author.\
Pal, Sujit ((Software engineer),) \author.\
Chollet, François, \writer of foreword.\
K 20
Kapoor, Amita,.
Deep learning with TensorFlow and Keras [[electronic resource] :] : build and deploy supervised,... unsupervised, deep, and reinforcement learning mod. / Amita, Kapoor ; author.: Gulli, Antonio,, Pal, Sujit ; writer of foreword. Chollet, François,. - Third edition. - [Б. м.] : PACKT PUBLISHING LIMITED,, 2022. - 1 online resource (698 pages) : : il. - (Expert insight). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/1FBB3E36-4936-46C3-A98B-56628384A89C. - ISBN 9781803245713 (electronic bk.). - ISBN 1803245719 (electronic bk.)
Параллельные издания: Print version: :
Рубрики: Machine learning.
Artificial intelligence.
Neural networks (Computer science)
Python (Computer program language)
Аннотация: Deep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow 2.x focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs based on Keras, and flexible model building on any platform. This book uses the latest TF 2.0 features and libraries to present an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical examples for the cloud, mobile, and large production environments. This book also shows you how to create neural networks with TensorFlow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), recurrent neural networks (RNNs), natural language processing (NLP), and graph neural networks (GNNs)), covers working example apps, and then dives into TF in production, TF mobile, and TensorFlow with AutoML.
Доп.точки доступа:
Gulli, Antonio, \author.\
Pal, Sujit ((Software engineer),) \author.\
Chollet, François, \writer of foreword.\
Страница 6, Результатов: 55