Recurrent Neural Networks (RNNs)
Recurrent Neural Networks, or RNNs, are a class of neural networks designed to process sequential data, where the output from one step feeds into the input of the next step within the network. Unlike traditional feedforward networks that treat each input independently, RNNs possess an internal memory that allows them to consider past information when processing current inputs. This makes them suitable for tasks like natural language processing, where the order of words is crucial, and speech recognition.
It's like a person reading a sentence, where understanding each new word depends on the words they've already read and remembered.
They are fundamental for processing sequential data, enabling AI to understand and generate content where context and order are critical.
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