Difference between revisions of "Bidirectional Long Short-Term Memory (BI-LSTM)"
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[http://www.google.com/search?q=Bidirectional+LSTM+machine+learning+ML+artificial+intelligence ...Google search] | [http://www.google.com/search?q=Bidirectional+LSTM+machine+learning+ML+artificial+intelligence ...Google search] | ||
| − | * [[Bidirectional Long Short-Term Memory (BI-LSTM) with Attention Mechanism]] | + | * [[Recurrent Neural Network (RNN)]] Variants: |
| − | * [[ | + | ** [[Long Short-Term Memory (LSTM)]] |
| + | ** [[Gated Recurrent Unit (GRU)]] | ||
| + | ** Bidirectional Long Short-Term Memory (BI-LSTM) | ||
| + | ** [[Bidirectional Long Short-Term Memory (BI-LSTM) with Attention Mechanism]] | ||
| + | ** [[Average-Stochastic Gradient Descent (SGD) Weight-Dropped LSTM (AWD-LSTM)]] | ||
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 12:01, 11 June 2020
YouTube search... ...Google search
- Recurrent Neural Network (RNN) Variants:
- Long Short-Term Memory (LSTM)
- Gated Recurrent Unit (GRU)
- Bidirectional Long Short-Term Memory (BI-LSTM)
- Bidirectional Long Short-Term Memory (BI-LSTM) with Attention Mechanism
- Average-Stochastic Gradient Descent (SGD) Weight-Dropped LSTM (AWD-LSTM)
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.
