  Blind Descent uses constrained but, guided approach to learn the weights. The
probability density function is non-zero in the infinite space of the dimension
(case in point: Gaussians and normal probability distribution functions). In
Blind Descent paper, some of the implicit ideas involving layer by layer
training and filter by filter training (with different batch sizes) were
proposed as probable greedy solutions. The results of similar experiments are
discussed. Octave (and proposed PyTorch variants) source code of the
experiments of this paper can be found at
https://github.com/PrasadNR/Attempted-Blind-Constrained-Descent-Experiments-ABCDE-
. This is compared against the ABCDE derivatives of the original PyTorch source
code of https://github.com/akshat57/Blind-Descent .
