Rnn
A recurrent neural network (RNN) is a class of artificial neural networks designed to handle sequential data. Unlike feedforward networks, RNNs maintain a hidden state that can carry information across time steps, allowing the network to incorporate context from previous inputs. At each time step t, the network receives an input x_t and updates its hidden state h_t based on h_{t-1} and x_t. This architecture makes RNNs suitable for tasks where the order and history of inputs matter.
In the simplest form, the hidden state is updated by h_t = f(W_xh x_t + W_hh h_{t-1} + b_h),
Training is performed by backpropagation through time, which unfolds the network across the sequence and applies
Common variants include bidirectional RNNs, which process input in forward and backward directions, and encoder–decoder or
Despite their strengths, RNNs can be computationally intensive and require careful design to capture long-range structure.