Recurrent Neural Networks
Recurrent Neural Networks (RNNs) are a type of neural network that can process sequential data by maintaining a state or memory of previous inputs. Unlike feedforward neural networks, which process inputs in a fixed order, RNNs can handle inputs of variable length and use the output of previous inputs as input for the current input. This makes them particularly useful for tasks such as speech recognition, language modeling, and time series prediction. RNNs can be trained using backpropagation through time, which is a variant of backpropagation that takes into account the temporal nature of the data. However, RNNs suffer from the vanishing gradient problem, which can make it difficult to learn long-term dependencies. To address this issue, several variants of RNNs have been proposed, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, which use gating mechanisms to selectively update the state.
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