Recurrent Neural Networks for Temporal Data Processing
The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the connections are not feed-forward ones only. In RNNs, connections between units form directed cycles, providing an implicit internal memory. Those RNNs are adapted to problems dealing with signals evolving...
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| Materyal Türü: | Online |
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| Dil: | İngilizce |
| Baskı/Yayın Bilgisi: |
IntechOpen
2021
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| Konular: | |
| Online Erişim: | ONIX_20210420_9789533076850_287 |
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| _version_ | 1869520212820230144 |
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| collection | Directory of Open Access Books |
| description | The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the connections are not feed-forward ones only. In RNNs, connections between units form directed cycles, providing an implicit internal memory. Those RNNs are adapted to problems dealing with signals evolving through time. Their internal memory gives them the ability to naturally take time into account. Valuable approximation results have been obtained for dynamical systems. |
| format | Online |
| id | doab-20.500.12854ir-64931 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | IntechOpen |
| publisherStr | IntechOpen |
| record_format | ojs |
| spelling | doab-20.500.12854ir-649312024-04-14T10:28:19Z Recurrent Neural Networks for Temporal Data Processing Cardot, Hubert Neural networks & fuzzy systems thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the connections are not feed-forward ones only. In RNNs, connections between units form directed cycles, providing an implicit internal memory. Those RNNs are adapted to problems dealing with signals evolving through time. Their internal memory gives them the ability to naturally take time into account. Valuable approximation results have been obtained for dynamical systems. 2021-04-20T15:05:16Z 2021-04-20T15:05:16Z 2011 book ONIX_20210420_9789533076850_287 9789533076850 9789535155218 https://directory.doabooks.org/handle/20.500.12854/64931 eng image/jpeg n/a https://www.intechopen.com/books https://mts.intechopen.com/storage/books/102/authors_book/authors_book.pdf IntechOpen IntechOpen 10.5772/631 10.5772/631 78a36484-2c0c-47cb-ad67-2b9f5cd4a8f6 9789533076850 9789535155218 IntechOpen 114 open access |
| spellingShingle | Neural networks & fuzzy systems thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence Recurrent Neural Networks for Temporal Data Processing |
| title | Recurrent Neural Networks for Temporal Data Processing |
| title_full | Recurrent Neural Networks for Temporal Data Processing |
| title_fullStr | Recurrent Neural Networks for Temporal Data Processing |
| title_full_unstemmed | Recurrent Neural Networks for Temporal Data Processing |
| title_short | Recurrent Neural Networks for Temporal Data Processing |
| title_sort | recurrent neural networks for temporal data processing |
| topic | Neural networks & fuzzy systems thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence |
| topic_facet | Neural networks & fuzzy systems thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence |
| url | ONIX_20210420_9789533076850_287 |