Fundamental Components of Deep Learning: A category-theoretic approach

Understand

Deep learning, despite its remarkable achievements, is still a young field.

• Like the early stages of many scientific disciplines, it is marked by the discovery of new phenomena, ad-hoc design decisions, and the lack of a uniform and compositional mathematical foundation.
• From the intricacies of the implementation of backpropagation, through a growing zoo of neural network architectures, to the new and poorly understood phenomena such as double descent, scaling laws or in-context learning, there are few unifying principles in deep learning.
• This thesis develops a novel mathematical foundation for deep learning based on the language of category theory.