Prerequisites IV: Category Theory - Pim de Haan

Categories for AI talk: Causal Model Abstraction & Grounding via Category Theory - by Taco CohenSee more

Categories for AI talk: Introduction to Categorical Cybernetics - by Jules HedgesSee more

Categories for AI 4: Geometric Deep Learning & Naturality - by Pim de HaanSee more

Categories for AI 1: Why Category Theory? By Bruno GavranovićSee more

Categories for AI talk: Category Theory Inspired by LLMs - by Tai-Danae BradleySee more

Categories for AI 2: Essential building blocks: Categories and Functors - Petar VeličkovićSee more

Prerequisites I: Groups, Representations & Equivariance - Maurice WeilerSee more

Categories for AI 3: Categorical Dataflow: Optics and Lenses as data structures for backpropagationSee more

Categories for AI talk: Neural network layers as parametric spans - by Pietro VertechiSee more

Cats4AI talk: Dynamic organizational systems: from deep learning to prediction markets, David SpivakSee more

What is Category Theory in mathematics? Johns Hopkins' Dr. Emily Riehl explainsSee more

Cats4AI talk: Sheaves for AI, Thomas GebhartSee more

Lecture 7: From Equivariance to Naturality - Pim de HaanSee more