Tag: 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.

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.