विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| लॉन्ग शॉर्ट-टर्म मेमोरी (LSTM)× | पुनरावर्ती तंत्रिका नेटवर्क× | |
|---|---|---|
| क्षेत्र | गहन अधिगम | गहन अधिगम |
| परिवार | Machine learning | Machine learning |
| उद्भव वर्ष≠ | 1997 | 1986–1990 |
| प्रवर्तक≠ | Hochreiter, S. & Schmidhuber, J. | Rumelhart, D. E.; Elman, J. L. |
| प्रकार≠ | Recurrent neural network with gated memory cells | Sequential neural network |
| मौलिक स्रोत≠ | Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. DOI ↗ | Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. DOI ↗ |
| उपनाम | LSTM, LSTM network, LSTM-RNN, long short-term memory RNN | RNN, Elman network, Jordan network, simple recurrent network |
| 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. | 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. |
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