| DC Field | Value | Language |
|---|
| dc.contributor.author | Wei, S. S. | - |
| dc.coverage.spatial | Минск | ru_RU |
| dc.date.accessioned | 2023-06-12T07:47:50Z | - |
| dc.date.available | 2023-06-12T07:47:50Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.citation | Wei, S. S. Development of recurrent neural networks / S. S. Wei // Технологии передачи и обработки информации : материалы Международного научно-технического семинара, Минск, март-апрель 2023 г. / Белорусский государственный университет информатики и радиоэлектроники; редкол.: В. Ю. Цветков [и др.]. – Минск, 2023. – С. 154–158. | ru_RU |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/51933 | - |
| dc.description.abstract | The Recurrent neural network (RNN) has been the main implementation of neural network sequence model, which is the standard processing tool for machine translation, machine question answering, and sequence video analysis, as well as the mainstream modeling tool for problems such as automatic handwriting synthesis, speech processing, and image generation. In the paper, the traditional RNN and the improved Long short-term memory (LSTM) are described in detail. | ru_RU |
| dc.language.iso | ru | ru_RU |
| dc.publisher | БГУИР | ru_RU |
| dc.subject | материалы конференций | ru_RU |
| dc.subject | RNN | ru_RU |
| dc.subject | neural network | ru_RU |
| dc.subject | LSTM | ru_RU |
| dc.title | Development of recurrent neural networks | ru_RU |
| dc.type | Article | ru_RU |
| Appears in Collections: | Технологии передачи и обработки информации : материалы Международного научно-технического семинара (2023)
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