discernable end point and, for continuous systems, can always be improved. Much like one might mix the colors of a Rubik's cube, here we use supervised learning to train a generic, deep neural network (NN) to mix a one-dimensional system using a slight modification of an existing algorithm in order to assess the potentialities and limitations of training and using ML systems to create mixing in other, more general cut-and-shuffle systems .