Deep Learning and Geometric Deep Learning: an introduction for
  mathematicians and physicists
R. Fioresi F. Zanchetta
In this expository paper we want to give a brief introduction, with few key
references for further reading, to the inner functioning of the new and
successfull algorithms of Deep Learning and Geometric Deep Learning with a
focus on Graph Neural Networks. We go over the key ingredients for these
algorithms: the score and loss function and we explain the main steps for the
training of a model. We do not aim to give a complete and exhaustive treatment,
but we isolate few concepts to give a fast introduction to the subject. We
provide some appendices to complement our treatment discussing Kullback-Leibler
divergence, regression, Multi-layer Perceptrons and the Universal Approximation
Theorem.