Saved in:
| Other Authors: | , , , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
London
Academic Press
2021
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| Series: | Hybrid computational intelligence for pattern analysis and understanding
|
| Subjects: | |
| Links: | https://learning.oreilly.com/library/view/-/9780128232682/?ar |
| Summary: | Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more. In recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models. |
| Item Description: | Includes bibliographical references and index. - Online resource; title from PDF title page (Ebook Central, viewed July 22, 2021) |
| Physical Description: | 1 online resource (xvii, 288 pages) illustrations. |
| ISBN: | 0128232684 9780128222263 0128222263 9780128232682 |
Staff View
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| spelling | Trends in deep learning methodologies algorithms, applications, and systems edited by Vincenzo Piuri, Sandeep Raj, Angelo Genovese, Rajshree Srivastava London Academic Press 2021 1 online resource (xvii, 288 pages) illustrations. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hybrid computational intelligence for pattern analysis and understanding Includes bibliographical references and index. - Online resource; title from PDF title page (Ebook Central, viewed July 22, 2021) Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more. In recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models. Artificial intelligence Neural networks (Computer science) Intelligence artificielle Réseaux neuronaux (Informatique) artificial intelligence Piuri, Vincenzo HerausgeberIn edt Raj, Sandeep HerausgeberIn edt Genovese, Angelo 1985- HerausgeberIn edt Srivastava, Rajshree HerausgeberIn edt 9780128222263 Erscheint auch als Druck-Ausgabe 9780128222263 |
| spellingShingle | Trends in deep learning methodologies algorithms, applications, and systems Artificial intelligence Neural networks (Computer science) Intelligence artificielle Réseaux neuronaux (Informatique) artificial intelligence |
| title | Trends in deep learning methodologies algorithms, applications, and systems |
| title_auth | Trends in deep learning methodologies algorithms, applications, and systems |
| title_exact_search | Trends in deep learning methodologies algorithms, applications, and systems |
| title_full | Trends in deep learning methodologies algorithms, applications, and systems edited by Vincenzo Piuri, Sandeep Raj, Angelo Genovese, Rajshree Srivastava |
| title_fullStr | Trends in deep learning methodologies algorithms, applications, and systems edited by Vincenzo Piuri, Sandeep Raj, Angelo Genovese, Rajshree Srivastava |
| title_full_unstemmed | Trends in deep learning methodologies algorithms, applications, and systems edited by Vincenzo Piuri, Sandeep Raj, Angelo Genovese, Rajshree Srivastava |
| title_short | Trends in deep learning methodologies |
| title_sort | trends in deep learning methodologies algorithms applications and systems |
| title_sub | algorithms, applications, and systems |
| topic | Artificial intelligence Neural networks (Computer science) Intelligence artificielle Réseaux neuronaux (Informatique) artificial intelligence |
| topic_facet | Artificial intelligence Neural networks (Computer science) Intelligence artificielle Réseaux neuronaux (Informatique) artificial intelligence |
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