Category theory is close to the perfect language. It can be used to describe many mathematical ideas, and see the relations between them, and their deeper structure. This is the first video in a course where we will carefully introduce the main ideas of category theory, and motivate them with lots of applications.
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A curated list of resources for studying category theory. As resources aimed at mathematicians are abundant, this list is aimed at materials whose target audience is not people with a graduate-level mathematics background.
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Catlab.jl is a framework for applied and computational category theory, written in the Julia language. Catlab provides a programming library and interactive interface for applications of category theory to scientific and engineering fields. It emphasizes monoidal categories due to their wide applicability but can support any categorical structure that is formalizable as a generalized algebraic theory.
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AlphaTensor is a novel AI solution to discover mathematical algorithms with Reinforcement Learning. Learn everything you need to know about AlphaTensor in this comprehensive introduction.
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As part of the African Master’s in Machine Intelligence (AMMI), we have delivered a course on Geometric Deep Learing (GDL100), which closely follows the contents of our GDL proto-book. We make all materials and artefacts from this course publicly available, as companion material for our proto-book, as well as a way to dive deeper into some of the contents for future iterations of the book.
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In this report, we will walk you through how you can leverage PyTorch Geometric along with Weights & Biases to analyse the Amazon products graph and recommend products from the graph.
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This is a list of papers studying the theory and application of lenses and optics, as they’re used in category theory and functional programming. Many of papers are in multiple fields and some of these fields overlap. Let me know what’s missing! (preferably by creating a pull request)
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