방법 비교

	Gated Recurrent Unit (GRU)	양방향 RNN
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2014	1997
창시자	Cho, K. et al.	Schuster, M. & Paliwal, K.K.
유형	Gated recurrent neural network unit	Recurrent neural network (sequence model)
원전	Cho, K. et al. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. EMNLP.	Schuster, M. & Paliwal, K.K. (1997). Bidirectional Recurrent Neural Networks. IEEE Transactions on Signal Processing, 45(11), 2673–2681.
별칭	Kapılı Tekrarlayan Birim (GRU), gated recurrent unit, gated recurrent network	Çift Yönlü RNN / BiLSTM / BiGRU, bidirectional recurrent neural network, BiLSTM, BiGRU
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.	A Bidirectional RNN, introduced by Schuster and Paliwal in 1997, processes a sequence in both forward and backward directions so that every position has access to its full surrounding context. With LSTM or GRU cells (BiLSTM/BiGRU) it is the standard approach for named-entity recognition, sequence labelling, and speech recognition.