Long Short-Term Memory (LSTM) networks are a type of Recurrent Neural Network (RNN) specifically designed to learn long-range dependencies in sequential data. They address the vanishing gradient problem that affects standard RNNs, which struggle to retain information over many time steps.
LSTMs introduce a special memory cell that maintains information over time and three types of gates that control the flow of information:
This gating mechanism allows LSTMs to selectively remember or forget information as needed, making them highly effective in tasks requiring memory of past inputs.
LSTMs are commonly used in:
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