# name
An approach to topos theory related to neural networks

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Treballs finals del Màster en Matemàtica Avançada, Facultat de Matemàtiques, Universitat de Barcelona: Any: 2025.  Director: Carles Casacuberta

# metadata.dc.description.abstract[].value
This work describes an application of topos theory and stacks to the semantic modeling of deep neural networks. Following the work of by Jean-Claude Belfiore and Daniel Bennequin, we present tools from topos theory to capture the learning process and semantic diffusion in a deep neural network architecture. We introduce the concepts of Grothendieck topoi, stacks and classifying topoi. Within this framework we establish conditions under which the semantic flow of information in a network can be transported across its layers.

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cc by-nc-nd (c) Diego Garrido Garcia, 2025

# metadata.dc.title[].value
An approach to topos theory related to neural networks