Trying to understand "Topos and Stacks of Deep Neural Networks"
This thread is about trying to understand preprint https://arxiv.org/abs/2106.14587 and its accompanying mathematical exposition chapter https://www.cambridge.org/core/books/abs/mathematics-for-future-computing-and-communications/mathematics-for-ai-categories-toposes-types/F75B023A3C42FAB5D4F186376216FC76
There is similar thread in nforum https://nforum.ncatlab.org/discussion/13133/understanding-preprint-topos-and-stacks-of-deep-neural-networks/#Item_0 and some Stackexchange questions.
The latest Stack question has been created this evening https://math.stackexchange.com/questions/4381378/how-omega-x-is-related-to-omega-as-subobject-classifier-trying-to-under about subobject classifiers. I hope that here or in NForum or StackExchange we can discuss these questions and help each other to get some understanding about this work.
I am a little bit shy to connect with the original authors, because I am beginner and maybe my questions are not at the level to spend time of serious researchers.
@alex and also @tomR
Please find some explanations in the file which is attached.
Jean-Claude Belfiore and Daniel Bennequin