Comparer des méthodes

	Réseau de neurones récurrent	Long Short-Term Memory (LSTM)
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	1986–1990	1997
Auteur d'origine	Rumelhart, D. E.; Elman, J. L.	Hochreiter, S. & Schmidhuber, J.
Type	Sequential neural network	Recurrent neural network with gated memory cells
Source fondatrice	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.	Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
Alias	RNN, Elman network, Jordan network, simple recurrent network	LSTM, LSTM network, LSTM-RNN, long short-term memory RNN
Apparentées	3	4
Résumé	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.	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.