In this chapter we present more specialized but very useful techniques. Autoencoders perform information processing though a bottleneck that determines the true information content of the data. They are simple mirror image-type network designs. Reservoir computing uses a random network reservoir that, while not trained, it provides useful states that help the general learning process. Physics informed networks can combine data with dynamical equations and learn from both.