방법 비교

	Gated Recurrent Unit (GRU)	LSTM
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2014	1997
창시자	Cho, K. et al.	Hochreiter, S. & Schmidhuber, J.
유형	Gated recurrent neural network unit	Recurrent neural network (gated memory cell)
원전	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.
별칭	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
관련	5	5
요약	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.