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Machine learningDeep learning / NLP / CV

Uhamishaji wa Mafunzo kwa Mitandao ya Neura ya Kimkunjo

Uhamishaji wa Mafunzo (Transfer Learning) kwa CNN hutumia tena mtandao wa neura wa kimkunjo ambao tayari umefunzwa kwa kutumia seti kubwa ya data — kwa kawaida ImageNet — na kurekebisha vigunduzi vyake vya vipengele vilivyojifunza ili viendane na seti mpya ya data lengwa, ambayo mara nyingi huwa ndogo. Hii inawawezesha watafiti kufikia utendaji mzuri wa utambuzi wa picha bila rasilimali kubwa za kompyuta na data zinazohitajika kufunza CNN kuanzia mwanzo.

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Vyanzo

  1. Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI: 10.1109/TKDE.2009.191
  2. Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems (NeurIPS), 27, 3320–3328. link

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Transfer Learning with Convolutional Neural Network (Feature Extraction and Fine-Tuning). ScholarGate. https://scholargate.app/sw/deep-learning/transfer-learning-with-convolutional-neural-network

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ScholarGateTransfer Learning with Convolutional Neural Network (Transfer Learning with Convolutional Neural Network (Feature Extraction and Fine-Tuning)). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/deep-learning/transfer-learning-with-convolutional-neural-network · Seti ya data: https://doi.org/10.5281/zenodo.20539026