One of the most consistent fonts of posts on this blog is The Guardian’s football trivia page The Knowledge. A particular reason for this is that the small contained questions lend themselves to small blogposts that I can turn around in an hour or two, as opposed to being endlessly redrafted until I lose interest.
However, I still sometimes don’t quite get round to finishing some of these posts, or have trouble justifying a blog post on a very small and ‘trivial’ answer to a question.
I’m extremely biased, but to me, one of the real success* stories in neuroscience over the last (just over) two decades has been in studying reward signals. Since the seminal 1997 paper, a lot of work has gone into figuring out how the brain assigns value to outcomes.
*ugh, maybe. This isn’t a blog post about that
My PhD project looks at novel ways of eliciting valuation behaviour to study these signals, but as a key part of the modelling involved in this work, it’s important to get a handle on reinforcement learning.
In my free time away from PhD and data science work, I (used to) enjoy rowing. Aside from obvious benefits like socialising, providing a (very intense) workout, seeing the outdoors at least a few times a week… there are really two things that I love(d) about rowing:
It’s the sport that is closest to a simple engineering problem. Going fast basically boils down to how in time and how hard you can get 1-8 guys to move an oar through the water.
A while ago (and also still a bit) racing bar charts were all the rage on data visualisation forums/twitter. Perhaps one of the real breakout examples is this tweet from the, always excellent, John Burn-Murdochat the Financial Times, looking at the most populous cities in the world since the middle ages:
A “Bar Chart Race” animation showing the changing ranks of the 10 biggest cities in the world since 1500.