A bit of precision

Linear factor models Linear factor models of stock returns are commonly used by practitioners and academics.

One of my least favourite activities associated with programming has always been configuration management.

Backpropagation is the fundamental algorithm that allows us to train neural networks, and autodifferentiation is the primary tool used to do this in practice.

I have been using Claude Code (and infrequently Codex) fairly frequently over the last few months on a variety of side projects.

For the last couple of years I have been using spaced repetition to revise and learn a variety of topics - linear algebra, probability, some ML concepts and a few programming libraries amongst other things.

Theory We are interested in solving the following convex optimisation problem: minxf0(x)s.t.fi(x)≤0,i=1,..,mAx=b where x is our (primal) optimisation variable, f0 is our objective function, fi(x)≤0 are our m inequality constraints, and Ax=b is an affine e…