Recurrent Neural Networks (RNNs), especially with Long Short-Term Memory (LSTM) units and Gated Recurrent Units (GRUs), address the vanishing gradient problem by using gates that control information flow. These gates help retain important data over long sequences, crucial for maintaining context and order. This ability significantly improves tasks like language translation, speech recognition, and stock price prediction by effectively learning and remembering long-term patterns.
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