Deep Learning and Geometric Deep Learning: an introduction for mathematicians and physicists
It serves as an introductory resource for mathematicians and physicists to understand these algorithms, but is incremental as it synthesizes existing knowledge without new contributions.
This expository paper provides a brief introduction to the inner workings of Deep Learning and Geometric Deep Learning algorithms, focusing on Graph Neural Networks, by explaining key concepts like score and loss functions and model training steps.
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.