পদ্ধতির তুলনা করুন

	গেটেড রিকারেন্ট ইউনিট (GRU)	পুনরাবৃত্ত নিউরাল নেটওয়ার্ক
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2014	1986–1990
প্রবর্তক	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y.	Rumelhart, D. E.; Elman, J. L.
ধরন	Recurrent neural network with gating	Sequential neural network
মৌলিক উৎস	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.
অপর নাম	GRU, GRU network, gated RNN, GRU cell	RNN, Elman network, Jordan network, simple recurrent network
সম্পর্কিত	3	3
সারসংক্ষেপ	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.