  Physicists have had a keen interest in the areas of Artificial Intelligence
(AI) and Machine Learning (ML) for some time now, with a special inclination
towards unravelling the mechanism at the core of the process of learning. In
particular, exploring the underlying mathematical structure of a neural net
(NN) is expected to not only help us in understanding the epistemological
meaning of `Learning' but also has the potential to unravel the secrets behind
the workings of the brain. Here, it is worthwhile to establish correspondences
and draw parallels between methods developed in core areas of Physics and the
techniques developed at the forefront of AI and ML. Although recent
explorations indicating a mapping between the Renormalisation Group(RG) and
Deep Learning(DL) have shown valuable insights, we intend to investigate the
relationship between RG and Autoencoders(AE) in particular. We will use
Transfer Learning(TL) to embed the procedure of coarse-graining in a NN and
compare it with the underlying mechanism of encoding-decoding through a series
of tests.
