قارن الطرق

	وحدة البوابات المتكررة (GRU)	الشبكة العصبية المتكررة
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2014	1986–1990
صاحب الطريقة	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y.	Rumelhart, D. E.; Elman, J. L.
النوع	Recurrent neural network with gating	Sequential neural network
المصدر التأسيسي	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.
الأسماء البديلة	GRU, GRU network, gated RNN, GRU cell	RNN, Elman network, Jordan network, simple recurrent network
ذات صلة	3	3
الملخص	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.	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.