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Neural network architecture for sequential data processing
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A recurrent neural network (RNN) is a class of neural networks where connections form directed cycles, allowing the network to maintain hidden state across sequential inputs. RNNs were the dominant architecture for language tasks through the mid-2010s. Variants like LSTM (1995) and GRU (2014) addressed the vanishing gradient problem. The transformer architecture largely replaced RNNs for language tasks after 2017.
A recurrent neural network (RNN) is a class of neural networks where connections form directed cycles, allowing the network to maintain hidden state across sequential inputs. RNNs were the dominant architecture for language tasks through the mid-2010s. Variants like LSTM (1995) and GRU (2014) addressed the vanishing gradient problem. The transformer architecture largely replaced RNNs for language tasks after 2017.
