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Illustration of an RNN. x i and y i are the input and output at the ith time step, respectively. In RNN, the output is dependent on (1) the current input, (2) the output from the previous time step, and (3) the network weights and biases. In other words, the RNN's output is dependent on the current and previous inputs together. This makes RNN suitable for analyzing sequential data. NN indicates neural network; RNN, recurrent neural network.
