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

Speaker: Bruno Gavranović Motivated by the recent emergence of category theory in machine learning, we teach a course on its philosophy, applications and outlook from the perspective of machine learning! Sign up for the course at: https://cats.for.ai/ In this third seminar, you will: - Understand the difference between a monoidal and a cartesian category - Get comfortable using their formal graphical language: string diagrams - Learn about lenses and optics, abstract interfaces for modelling bidirectional data flow - See examples of lenses and optics modelling backpropagation, gradient descent, value iteration and more - Understand how the chain rule is a special case of lens composition These lectures will help explain key parts of Categorical Foundations of Gradient-Based Learning (ESOP 2022) https://arxiv.org/abs/2103.01931