قارن الطرق

	وحدة التكرار المسورة (GRU)	نموذج التسلسل إلى التسلسل
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2014	2014
صاحب الطريقة	Cho, K. et al.	Sutskever, I.; Cho, K.
النوع	Gated recurrent neural network unit	Encoder-decoder neural network (deep learning)
المصدر التأسيسي	Cho, K. et al. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. EMNLP.	Sutskever, I., Vinyals, O. & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. NeurIPS.
الأسماء البديلة	Kapılı Tekrarlayan Birim (GRU), gated recurrent unit, gated recurrent network	Dizi-Dizi Modeli (Seq2Seq — Encoder-Decoder), encoder-decoder model, seq2seq, sequence to sequence learning
ذات صلة	5	5
الملخص	The Gated Recurrent Unit (GRU) is a gated recurrent neural network cell introduced by Cho and colleagues in 2014 that captures long-range dependencies in sequential data using update and reset gates, achieving performance comparable to LSTM with fewer parameters.	The sequence-to-sequence (Seq2Seq) model, introduced by Sutskever, Vinyals and Le and by Cho and colleagues in 2014, is an encoder-decoder neural network that maps a variable-length input sequence to a variable-length output sequence. It is the foundation of machine translation, text summarization, dialogue systems and code generation.