recurrent neural networks rnns produce either an output for every entity in the input sequence (many-to-manyor single output for the entire sequence (many-to-one)as shown figure - let' consider an rnn that produces one output for every entity in the input (essentially referring to the unrolled network shown in figure - figure - an unrolled rnn (many-to-many)representing part of figure - the rnn can be described using the following equationsh tanh ux wh yt softmax vht is the weight to the input to the networkv is the weight to the output from the activation functionand is the weight matrix for the current hidden state the following points about the rnn equations should be noted the rnn computation involves computing the hidden state for an entity in the sequence this is denoted by ( the computation of (tuses the corresponding input at entity (tand the previous hidden state (