LSTM Networks
Learn about LSTM networks and their structure as advanced recurrent neural networks designed to overcome the limitations of simple RNNs. Understand the components like cell state, input, forget, and output gates, and grasp how feedforward training and backpropagation through time optimize these networks. You'll gain insights into handling issues such as vanishing and exploding gradients, essential for effective deep learning model training.
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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 RNN contains 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. ...