  We look at the eigenvalues of the Hessian of a loss function before and after
training. The eigenvalue distribution is seen to be composed of two parts, the
bulk which is concentrated around zero, and the edges which are scattered away
from zero. We present empirical evidence for the bulk indicating how
over-parametrized the system is, and for the edges that depend on the input
data.
