Bidirectional Long Short-Term Memory (BI-LSTM)

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• 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)
  • 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.