विधियों की तुलना करें

	गेटेड रिकरेंट यूनिट (GRU)	पुनरावर्ती तंत्रिका नेटवर्क
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2014	1986–1990
प्रवर्तक	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y.	Rumelhart, D. E.; Elman, J. L.
प्रकार	Recurrent neural network with gating	Sequential neural network
मौलिक स्रोत	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.
उपनाम	GRU, GRU network, gated RNN, GRU cell	RNN, Elman network, Jordan network, simple recurrent network
संबंधित	3	3
सारांश	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.