recurrent neural networks the output yt is computed using the hidden state ( while the current hidden state is being computeda set of weights are associated with the input and the previous hidden state this is denoted by and wrespectively there is also bias termdenoted by similarlywhile computing the outputa set of weights are also associated with the current hidden state this is denoted by there is also bias termdenoted by alsowhile computing the hidden statethe tanh activation function (introduced in earlier is used the softmax activation function is used in the computation of the output the rnnas described by the equationscan process an arbitrarily large input sequence the parameters of the rnn--uwvbcetc -are shared across the computation of the hidden layer and output value (for each of entities in the sequencefigure - illustrates the rnn note the recurrence relationship with the self-loop at the hidden state