As with feedforward networks, we'll use a training set, a loss function, and backpropagation to obtain the gradients needed to adjust the weights in these recurrent networks. As shown in Fig. 9 .3, we now have 3 sets of weights to update: W, the weights from the input layer to the hidden layer, U, the weights from the previous hidden layer to the current hidden layer, and finally V, the weights from the hidden layer to the output layer. Fig. 9 .5 highlights two considerations that we didn't have to worry about with backpropagation in feedforward networks. First, to compute the loss function for the output at time t we need the hidden layer from time t − 1. Second, the hidden layer at time t influences both the output at time t and the hidden layer at time t + 1 (and hence the output and loss at t + 1). It follows from this that to assess the error accruing to h t , we'll need to know its influence on both the current output as well as the ones that follow.