Trends in deep learning methodologies : algorithms, applications, and systems
Zapisane w:
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| Kolejni autorzy: | , , |
| Format: | E-Book |
| Język: | Anglais |
| Wydane: |
London ; San Diego (Calif.) ; Cambridge (Mass.) :
Academic Press : Elsevier.
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| Hasła przedmiotowe: | |
| Autres localisations: | Voir dans le Sudoc |
| Streszczenie: | "Trends in deep learning methodologies [...] 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 |
| Dostęp online: | Accès à l'E-book |
| Streszczenie: | "Trends in deep learning methodologies [...] 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 |
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| Deskrypcja: | Couverture (https://static2.cyberlibris.com/books_upload/136pix/9780128232682.jpg). |
| Bibliografia: | Réf. bibliographiques en fin de chapitres. Index. |
| ISBN: | 9780128232682 |
| Ograniczenie dostępu: | L'accès en ligne est réservé aux établissements ou bibliothèques ayant souscrit l'abonnement |