What are recurrent neural networks, and how are they used?

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Recurrent neural networks (RNNs) are specialized neural networks designed to process sequential data, such as time series or language. They maintain a memory of previous inputs, making them suitable for tasks like speech recognition and language modeling. RNNs can capture temporal dependencies, allowing them to predict future data points based on past information. However, they can suffer from issues like vanishing gradients, which limit their ability to learn long-term dependencies. Variants like LSTM (Long Short-Term Memory) networks address these challenges, enhancing RNN performance.

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