In recurrent neural networks, hidden states act as a short-term memory, retaining information from previous inputs to influence future outputs. They capture sequential patterns, enabling models to understand context in tasks like language translation or speech recognition. Data scientists and AI engineers benefit by improving temporal predictions, while end-users get smarter, more coherent AI interactions.
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