  It has been demonstrated earlier that universal computation is 'almost
surely' chaotic. Machine learning is a form of computational fixed point
iteration, iterating over the computable function space. We showcase some
properties of this iteration, and establish in general that the iteration is
'almost surely' of chaotic nature. This theory explains the observation in the
counter intuitive properties of deep learning methods. This paper demonstrates
that these properties are going to be universal to any learning method.
