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

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

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

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

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

I can't STOP reading these Machine Learning Books!See more

Ashneer views on Ai & jobs (shocking😱)See more

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

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

GEOMETRIC DEEP LEARNING BLUEPRINTSee more

Categories for AI 5: Monoids, Monads, Mappings, and lstMs - by Andrew DudzikSee more