Long Short-Term Memory (LSTM) is a type of recurrent neural network architecture that is specifically designed to model and predict sequences of data over time. It addresses the limitations of traditional recurrent neural networks by utilizing memory cells, which help retain information for longer periods, making it suitable for tasks like speech recognition and natural language processing where context is crucial.
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