Recurrent neural networks (RNNs) are a class of neural networks designed to process sequential data. Unlike feedforward networks, RNNs have feedback connections, allowing them to maintain a 'memory' of past inputs. This makes them suitable for tasks such as natural language processing (NLP), speech recognition, and time series analysis. They work by applying a function to each element in the sequence, while also incorporating information from the previous steps in the sequence, enabling them to learn temporal dependencies. Common use cases include machine translation, sentiment analysis, and generating text.
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