Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| LSTM× | Residuelle nevrale nettverk× | |
|---|---|---|
| Fagfelt | Dyp læring | Dyp læring |
| Familie | Machine learning | Machine learning |
| Opprinnelsesår≠ | 1997 | 1986–1990 |
| Opphavsperson≠ | Hochreiter, S. & Schmidhuber, J. | Rumelhart, D. E.; Elman, J. L. |
| Type≠ | Recurrent neural network (gated memory cell) | Sequential neural network |
| Opprinnelig kilde≠ | 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 ↗ |
| Alias | LSTM (Uzun Kısa Dönem Bellek Ağı), long short-term memory, LSTM network, recurrent neural network with memory cells | RNN, Elman network, Jordan network, simple recurrent network |
| Relaterte≠ | 5 | 3 |
| Sammendrag≠ | LSTM (Long Short-Term Memory) is a recurrent neural network architecture, introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997, that can learn long-term dependencies in sequential data and is widely used for time-series and sequence prediction. It keeps an internal memory that lets information persist across many time steps. | 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. |
| ScholarGateDatasett ↗ |
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