পদ্ধতির তুলনা করুন

	পুনরাবৃত্ত নিউরাল নেটওয়ার্ক	লং শর্ট-টার্ম মেমরি (এলএসটিএম)
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1986–1990	1997
প্রবর্তক	Rumelhart, D. E.; Elman, J. L.	Hochreiter, S. & Schmidhuber, J.
ধরন	Sequential neural network	Recurrent neural network with gated memory cells
মৌলিক উৎস	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.
অপর নাম	RNN, Elman network, Jordan network, simple recurrent network	LSTM, LSTM network, LSTM-RNN, long short-term memory RNN
সম্পর্কিত	3	4
সারসংক্ষেপ	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.