The purpose of this thesis is to provide practical, high-performance tools for working with string diagrams for the specific application of machine learning. The thesis consists of two main lines of research towards this aim. In Part I, we define a family of categories of differentiable circuits suitable for machine learning. This construction is made in a modular way: we first give an alternative axiomatisation of Reverse Derivative categories which is then used to prove a functional completeness result showing that these circuits are sufficiently expressive. We then show how ‘gradient-like’ learning can be understood in terms of morphisms of these categories and discuss how to generalise gradient-based methods as applied to neural networks to new settings by varying the ‘underlying arithmetic’ of models. Part II of the thesis is concerned with how to represent and manipulate large string diagrams, specifically with an eye to those defined in Part I. We develop data structures and define efficient algorithms for tensor and composition of string diagrams in terms of simple linear-algebraic operations. We show that complexity of operations is linear in the size of the resulting diagram and validate our claims with empirical evidence that our approach can handle diagrams constructed from millions of generators. Finally, we give a graphical calculus allowing terms of non-strict monoidal categories to be represented by our data structure, which in turn yields novel proofs of Mac Lane’s strictness and coherence theorems.