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

R. Fioresi; F. Zanchetta

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arXiv · 2305.05601

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

R. Fioresi·F. Zanchetta

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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.

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R. Fioresi, F. Zanchetta. 2023-05-09. Deep Learning and Geometric Deep Learning: an introduction for mathematicians and physicists. https://arxiv.org/abs/2305.05601

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