Summary: Understanding Long Short-Term Memory Networks
Learn the structure and function of Long Short-Term Memory networks, including gates and states. Understand how LSTMs handle long-term dependencies better than RNNs, and discover techniques for enhancing LSTM performance like greedy sampling, beam search, word vector integration, BiLSTMs, and variants such as peephole connections and GRUs.
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In this chapter, we learned about LSTM networks. First, we discussed what an LSTM is and its high-level architecture. We also delved into the detailed computations that take place in an LSTM and discussed the computations through an example.
Composition of LSTM
We saw that an LSTM is composed mainly of five different things:
Cell state: This is the internal cell state of an LSTM cell.
Hidden state: The external hidden state is used to calculate predictions.
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