Recurrent Neural Networks (RNNs) for Sequence Modeling
Recurrent Neural Networks (RNNs) are a class of neural networks designed to process sequential data, where the output from the previous step is fed as input to the current step. This 'memory' mechanism allows RNNs to learn dependencies across time or space, making them suitable for tasks like natural language processing, speech recognition, and time series prediction, by effectively handling variable-length sequences and context.
It's like reading a book where your understanding of the current sentence depends on what you just read in the previous sentences.
RNNs are fundamental for AI systems that need to understand and generate sequential data, which is pervasive in language, audio, and time series applications.
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