The APIs for MLP and CNN architectures are limited. Both architectures accept a fixed-size tensor as input and produce a fixed-size tensor as output; and they perform the transformation from input to output in a fixed number of steps given by the number of layers in the model. RNNs don't have this limitation—you can have sequences in the input, the output, or both. This means that RNNs can be arranged in many ways to solve specific problems.
As we have learned, RNNs combine the input vector with the previous state vector to produce a new state vector. This can be thought of as similar to running a program with some inputs and some internal variables. Thus RNNs can be thought of as essentially describing computer programs. In fact, it has been shown that RNNs are turing complete (for more information refer to the article: On the Computational Power of Neural Nets, by H. T. Siegelmann and E. D. Sontag, proceedings of the fifth annual workshop on computational learning theory...
