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Machine learningDeep learning / NLP / CV

LSTM Iliyorekebishwa

LSTM Iliyorekebishwa hubadilisha mtandao wa kumbukumbu wa muda mrefu (Long Short-Term Memory - LSTM) uliofunzwa awali kwenye hifadhidata kubwa kuelekea kazi maalum inayofuata — kama vile uainishaji wa maandishi, uchanganuzi wa hisia, au uwekaji lebo wa mfuatano — kwa kuendeleza mafunzo kwenye data yenye lebo maalum kwa kazi husika. Njia hii, iliyopata umaarufu kupitia mfumo wa ULMFiT, hufikia utendaji kazi wenye nguvu hata pale data yenye lebo inapokuwa adimu.

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Vyanzo

  1. Howard, J., & Ruder, S. (2018). Universal Language Model Fine-tuning for Text Classification. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), 328–339. DOI: 10.18653/v1/P18-1031
  2. Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. DOI: 10.1162/neco.1997.9.8.1735

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Fine-Tuned Long Short-Term Memory Network. ScholarGate. https://scholargate.app/sw/deep-learning/fine-tuned-lstm

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Imerejelewa na

ScholarGateFine-Tuned LSTM (Fine-Tuned Long Short-Term Memory Network). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/deep-learning/fine-tuned-lstm · Seti ya data: https://doi.org/10.5281/zenodo.20539026