So sánh phương pháp

	GRU Tinh chỉnh (Fine-Tuned GRU)	Mạng nơ-ron hồi quy
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2014 (GRU); fine-tuning practice established 2010s	1986–1990
Người khởi xướng	Cho, K. et al. (GRU); fine-tuning practice from transfer learning literature	Rumelhart, D. E.; Elman, J. L.
Loại	Sequence model with transfer learning	Sequential neural network
Công trình gốc	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.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
Tên gọi khác	Fine-Tuned GRU, GRU Fine-Tuning, Domain-Adapted GRU, GRU Transfer Learning	RNN, Elman network, Jordan network, simple recurrent network
Liên quan	5	3
Tóm tắt	Fine-Tuned GRU adapts a Gated Recurrent Unit network — pre-trained on a large source dataset — to a specific target task or domain by continuing training on domain-specific labeled data. This combines the sequential memory capacity of GRUs with the efficiency gains of transfer learning, achieving strong performance even when labeled target data is scarce.	A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models.