LSTM Networks
Learn how Long Short Term Memory networks work as advanced recurrent neural networks designed to overcome challenges with long-term dependencies in sequence data. Understand the key components like cell state, gates, and hidden states, along with training techniques to address gradient problems.
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LSTM Networks
LSTM stands for Long Short Term Memory and they are a special kind of RNN. They were introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997. They have proven to work very well on a large variety of problems in the field. They overcome the limitations of Naïve RNNs which fail to deal with long term dependencies in the sequences. In a simple RNN we have repeating blocks as shown in the figure below. This RNNcontains a single tanh layer.
We have the same chain like structure in LSTMs but the repeating module has a different structure in LSTMs. You can see this in the diagram below.
In the above diagram we have the following representations.
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Each line carries a vector.
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Pink circles ...