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A variation of Recurrent Neural Networks (RNNs) designed to address the vanishing gradient problem. LSTMs use special units called memory cells that can store information for long periods, allowing them to capture long-range dependencies in sequential data. This makes them particularly effective for tasks such as speech recognition, language modeling, and time series prediction.