Recurrent Neural Networks (RNNs)
Recurrent Neural Networks (RNNs) are a class of neural networks designed to process sequential data, meaning data where the order of elements matters. Unlike traditional neural networks, RNNs have loops within their architecture, allowing information to persist from previous steps and enabling them to 'remember' past inputs in a sequence. This makes them suitable for tasks involving time-series or natural language.
It's like reading a story where each new sentence you read helps you understand the context of the next one, building on what came before.
RNNs are crucial for tasks that involve understanding context and dependencies in sequences, like natural language processing, speech recognition, and time-series forecasting.
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