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Advanced analytics and deep learning models /
Advanced Analytics and Deep Learning Models The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc. Adva...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, NJ :
John Wiley & Sons,
2022.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 245 | 0 | 0 | |a Advanced analytics and deep learning models / |c edited by Archana Mire, Shaveta Malik and Amit Kumar Tyagi. |
| 264 | 1 | |a Hoboken, NJ : |b John Wiley & Sons, |c 2022. | |
| 264 | 4 | |c ©2022 | |
| 300 | |a 1 online resource | ||
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| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Online resource; title from digital title page (viewed on May 11, 2022). | |
| 520 | |a Advanced Analytics and Deep Learning Models The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc. Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools. However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. Deep learning can be used to deal with all kinds of problems and challenges that include collecting unlabeled and uncategorized raw data, extracting complex patterns from a large amount of data, retrieving fast information, tagging data, etc. This book contains 16 chapters on artificial intelligence, machine learning, deep learning, and their uses in many useful sectors like stock market prediction, a recommendation system for better service selection, e-healthcare, telemedicine, transportation. There are also chapters on innovations and future opportunities with fog computing/cloud computing and artificial intelligence. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in healthcare, telemedicine, transportation, and the financial sector. The book will also be a great source for software engineers and advanced students who are beginners in the field of advanced analytics in deep learning. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Deep learning (Machine learning) | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Big data. | |
| 650 | 6 | |a Apprentissage profond. | |
| 650 | 6 | |a Intelligence artificielle. | |
| 650 | 6 | |a Données volumineuses. | |
| 650 | 7 | |a artificial intelligence. |2 aat | |
| 650 | 7 | |a Artificial intelligence |2 fast | |
| 650 | 7 | |a Big data |2 fast | |
| 650 | 7 | |a Deep learning (Machine learning) |2 fast | |
| 700 | 1 | |a Mire, Archana, |e editor. | |
| 700 | 1 | |a Malik, Shaveta, |e editor. | |
| 700 | 1 | |a Tyagi, Amit Kumar, |e editor. | |
| 776 | 0 | 8 | |i Print version: |z 1119791758 |z 9781119791751 |w (OCoLC)1252050379 |
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