Machine Learning

Most of the content here I prepared for giving a talk on Bayesian inference. Some background stuff didn’t fit in the (introductory) talk itself, so this is more of a space for me to put the rest. Below, I will discuss a few things: Firstly how Bayesian inference leads into the need to sample from high-dimensional functions. I’ll then discuss some peculiarities of high-dimensional spaces. These will be used when I discuss a few different solution methods, to explain why and when they will work or fail....

Fourier Analysis for DS People going into Data Science as a profession tend to come from a diverse set of technical backgrounds. However, the last few years more and more come from specifically Data Science masters programs. Fourier Analysis is a topic that tends to not be discussed in these settings. I think it’s still interesting enough to dive into for a bit, both because of its interesting mathematics and because it can give a lot of insight when working with time-series data....

Understanding of Neural nets from first-principles: Brain dump So I was reading my company’s IT newsletter the other day where one of the topics was sparse modeling (discussing this Forbes article) and it got me thinking again about some things I was reading the past months, about trying to understand how and why (mainly) Deep Learning works. First-principles In a way, sure we know how it works on the microscopic level of each individual neuron (activation functions, matrix multiplications, gradient descent and all that), and we also often describe it at a high level (where we tend to greatly anthropomorphize it: “the model learned to do X because in all its examples it saw this object from the same angle, ....