Recurrent Neural Networks (RNNs) for Sequence Modeling
Recurrent Neural Networks (RNNs) are a type of neural network designed to process sequential data, where the output from one step is fed back as input to the next step. This 'memory' allows RNNs to capture dependencies and context across items in a sequence, such as words in a sentence or events in a time series. While basic RNNs struggle with long-term dependencies, variants like LSTMs and GRUs address these limitations.
Think of it as a person reading a story, where understanding each new word depends on remembering previous words and sentences.
RNNs are crucial for tasks involving sequential data like natural language understanding, speech recognition, and time series prediction.
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