recurrent neural networks figure - unrolling the rnn corresponding to figure - (single outputthe unrolling process operates on the assumption that the length of the input sequence is known beforehand and based on that the recurrence is unrolled once the rnn is unrolledwe essentially have non-recurrent neural network the parameters to be learned--uwvbc etc (denoted in dark in figure - )--are shared across the computation of the hidden layer and output value we have seen such parameter sharing earlier in the context of convolutional neural networks given an input and output of given size (for exampletwhich is assumed to be in figures - through - )we can unroll an rnn and compute gradients for the parameters to be learned with respect to loss function (as described in earlier thustraining an rnn is simply first unrolling the rnn for given size of input and corresponding expected outputand then training the unrolled rnn by computing the gradients and using stochastic gradient descent