השוואת שיטות

	רשת נוירונים רקורנטית	Gated Recurrent Unit (GRU)
תחום	למידה עמוקה	למידה עמוקה
משפחה	Machine learning	Machine learning
שנת המקור	1986–1990	2014
הוגה השיטה	Rumelhart, D. E.; Elman, J. L.	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y.
סוג	Sequential neural network	Recurrent neural network with gating
מקור מכונן	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.	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.
כינויים	RNN, Elman network, Jordan network, simple recurrent network	GRU, GRU network, gated RNN, GRU cell
קשורות	3	3
תקציר	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.	The Gated Recurrent Unit (GRU), introduced by Cho et al. in 2014, is a streamlined recurrent neural network that uses two learned gates — an update gate and a reset gate — to selectively retain or discard information across time steps, enabling effective sequence modelling with fewer parameters than LSTM.