Tag: Math

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.

This is a follow-up to the Impractical Time Telling post.

Impractical Time Telling II: The Practicalities #

The plan was to do this in some downtime over the Christmas holidays, but the stars already aligned a few weeks ago. I found a Garmin Instinct 2S smartwatch in the house, and Garmin actually has a good setup (called Connect IQ) for developers to create their own apps, watchfaces, et cetera.

Playing with strings #

Knot theory is one of those topics where you start out by asking a very simple and natural question, follow a thread (hehe), then look around you and realize you’re knee-deep in at least 5 fields of math.

Vector Product Aesthetics #

The Beauty: Inner Product #

Think back for a minute to your first Linear Algebra course: Remember how nice inner products were to compute? Try to think of how to do it off the top of your head. If it’s a bit blurry: it’s just taking each component of the vectors, multiplying them and adding all the results: $$\mathbf{a \cdot b} = \sum_{i=0}^{N} a_i b_i$$ Calculating it gives you a scalar that says something about the angle between the two. It is as simple to do in 687D as it is in 2D.

Impractical Time Telling #

You know how sometimes problems are just completely solved and thus boring? Like telling the time: We used to have sundials, now we have quartz watches, digital clocks… Yawn. A while ago I got a watch that was quite funky, although a bit of a challenge to read.

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.