مقایسهٔ روش‌ها

	واحد بازگشتی دروازه‌ای (GRU)	LSTM
حوزه	یادگیری عمیق	یادگیری عمیق
خانواده	Machine learning	Machine learning
سال پیدایش	2014	1997
پدیدآور	Cho, K. et al.	Hochreiter, S. & Schmidhuber, J.
نوع	Gated recurrent neural network unit	Recurrent neural network (gated memory cell)
منبع بنیادین	Cho, K. et al. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. EMNLP.	Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.
نام‌های دیگر	Kapılı Tekrarlayan Birim (GRU), gated recurrent unit, gated recurrent network	LSTM (Uzun Kısa Dönem Bellek Ağı), long short-term memory, LSTM network, recurrent neural network with memory cells
مرتبط	5	5
خلاصه	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.	LSTM (Long Short-Term Memory) is a recurrent neural network architecture, introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997, that can learn long-term dependencies in sequential data and is widely used for time-series and sequence prediction. It keeps an internal memory that lets information persist across many time steps.