Machine learningDeep learning / NLP / CV

UFine-Tuned LSTM

Fine-Tuned LSTM (UFine-Tuned LSTM) prilagođava prethodno obučeni Long Short-Term Memory (LSTM) mrežni model na velikom korpusu za specifičan ciljni zadatak — kao što je klasifikacija teksta, analiza sentimenta ili označavanje sekvenci — nastavljajući obuku na označenim podacima specifičnim za zadatak. Popularizovan od strane ULMFiT okvira, ovaj pristup postiže snažne performanse čak i kada su označeni podaci oskudni.

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Izvori

  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

Kako citirati ovu stranicu

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

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ScholarGateFine-Tuned LSTM (Fine-Tuned Long Short-Term Memory Network). Preuzeto 2026-06-15 sa https://scholargate.app/sr/deep-learning/fine-tuned-lstm · Skup podataka: https://doi.org/10.5281/zenodo.20539026