Difference between revisions of "Bidirectional Long Short-Term Memory (BI-LSTM)"
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** [[Bidirectional Long Short-Term Memory (BI-LSTM) with Attention Mechanism]] | ** [[Bidirectional Long Short-Term Memory (BI-LSTM) with Attention Mechanism]] | ||
** [[Average-Stochastic Gradient Descent (SGD) Weight-Dropped LSTM (AWD-LSTM)]] | ** [[Average-Stochastic Gradient Descent (SGD) Weight-Dropped LSTM (AWD-LSTM)]] | ||
| + | ** [[Hopfield Network (HN)]] | ||
The purpose of the Bi-LSTM is to look at a particular sequences both from front-to-back as well as from back-to-front. In this way, the network creates a context for each character in the text that depends on both its past as well as its future. | The purpose of the Bi-LSTM is to look at a particular sequences both from front-to-back as well as from back-to-front. In this way, the network creates a context for each character in the text that depends on both its past as well as its future. | ||
Revision as of 12:15, 11 June 2020
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- Recurrent Neural Network (RNN) Variants:
The purpose of the Bi-LSTM is to look at a particular sequences both from front-to-back as well as from back-to-front. In this way, the network creates a context for each character in the text that depends on both its past as well as its future.
