May 22, 2023

A Bayesian beer tasting

Me and my mates are big fans of craft beer, and from time to time organise our own beer tastings. Each participant gets to give a single score between 1 and 5 (half point increments are allowed) to each beer and afterwards we compare scores and see which beer came out on top. Sour beer and imperial stouts are some of our favourite styles, but this time we decided to do something different: A blind tasting with the cheapest lager we could find. Read more

November 9, 2022

Split-Apply-Combine Is More Flexible Than You Think

The data frame is the workhorse of anything related to data and is an incredibly flexible tool. It can be used for almost everything: organising, filtering, transforming, plotting, etc. In this article we are going to see that it is probaly even more flexible than people think! Good old data frames When you think of data frames you might be thinking of something square with numbers in it. Perhaps something like Read more

October 13, 2022

When Neural ODEs fail

Over the years I have received a lot of emails in response to my post about neural ODEs where people ask for advice on a particular pitfall when applying neural ODEs on regression style problems. So here is a (long overdue) blog post to address that! Code can be found here. The first lesson of machine learning If you are a machine learning practitioner I’m sure you’ve been told to “start simple”. Read more

October 15, 2020

A penguin fish-recommender systems using multi-armed bandits pt. 2

In the previous article we were introduced to the Palmer Penguins, their eating habits, and built a recommender system that would serve them their favourite fish. However, the system we built was overly simplistic and assumed a single favourite fish across the population. As we all know, there is no single best of anything. It all comes down to personal preference. In this article we are going to see how to improve the system through contextual multi-armed bandits, which will allow learning the penguins preferences much more granularly than a population average. Read more

September 22, 2020

A penguin fish-recommender systems using multi-armed bandits pt. 1

With the climate crisis raging on, I bet you are all thinking about the penguins. How are they going to find food with their ecosystem collapsing? It only makes sense that us humans take our responsibility to feed them. However, it would be preferable not to feed them manually, since Antarctica is a somewhat inhospitable place. And what kind of fish do penguins like to eat anyway? Clearly, the best (and most practical) solution to this problem is to apply machine learning, specifically multi-armed bandits, to build an autonomous self-improving fish-recommender system. Read more

© Sebastian Callh 2020