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Msaidizi
Machine learningDeep learning / NLP / CV

GRU inayobadilika na Nyanja

GRU inayobadilika na Nyanja inachanganya usanifu wa Gated Recurrent Unit na mbinu za upangiliaji wa nyanja ili kufundisha mfumo wa mlolongo kwenye nyanja ya chanzo yenye lebo na kuihamisha hadi nyanja lengwa tofauti lakini inayohusiana, ikipunguza uharibifu wa utendaji unaosababishwa na mabadiliko ya usambazaji. Inatumika sana katika kazi za NLP kama vile uchambuzi wa hisia baina ya nyanja, utambuzi wa majina, na uainishaji wa maandishi ambapo data ya nyanja lengwa yenye lebo ni adimu.

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Vyanzo

  1. Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In Proceedings of EMNLP 2014 (pp. 1724–1734). Association for Computational Linguistics. link
  2. Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2016). Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17(1), 2096–2030. link

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Domain-Adaptive Gated Recurrent Unit Network. ScholarGate. https://scholargate.app/sw/deep-learning/domain-adaptive-gru

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ScholarGateDomain-adaptive GRU (Domain-Adaptive Gated Recurrent Unit Network). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/deep-learning/domain-adaptive-gru · Seti ya data: https://doi.org/10.5281/zenodo.20539026