# Deep Learning and Geometric Deep Learning: an introduction for mathematicians and physicists

**Authors:** R. Fioresi, F. Zanchetta  
**arXiv:** [2305.05601](https://arxiv.org/abs/2305.05601)  
**Date:** 2023-05-09  
**Summary page:** https://www.summarizepaper.com/fr/paper/2305.05601v1/

## Abstract

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.