विधियों की तुलना करें

	लॉन्ग शॉर्ट-टर्म मेमोरी (LSTM)	गेटेड रिकरेंट यूनिट (GRU)
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	1997	2014
प्रवर्तक	Hochreiter, S. & Schmidhuber, J.	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y.
प्रकार	Recurrent neural network with gated memory cells	Recurrent neural network with gating
मौलिक स्रोत	Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.	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.
उपनाम	LSTM, LSTM network, LSTM-RNN, long short-term memory RNN	GRU, GRU network, gated RNN, GRU cell
संबंधित	4	3
सारांश	Long Short-Term Memory (LSTM) is a gated recurrent neural network architecture introduced by Hochreiter and Schmidhuber in 1997. It was designed to learn dependencies across long sequences by using dedicated memory cells and three learned gates — forget, input, and output — that control what information is retained, updated, or passed forward at each time step.	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.