This algorithm is named after George Edward Pelham Box and Mervin Edgar Muller, who published it on a two-page paper in 1958. The idea was not original, though: it appeared already in the 1934 book Fourier Transforms in the Complex Domain, by Raymond E. A. C. Paley and Norbert Wiener. Stigler's law strikes again!
We don't know for sure when the Euclidean Algorithm was created nor by whom, but it was made famous around 300 BC by the Elements – the magnum opus of Greek mathematician Euclid. Wikipedia describes it as “one of the oldest algorithms in common use” and Knuth affectionately calls it “the granddaddy of all algorithms”.
This is a repository of more or less random programming things, made for my own amusement and edification. I don't know how this will evolve over time (if at all), but I envision this as a collection of interactive visual explanations of algorithms and data structures.
🎄 Feliz Natal! 🎅
(In Portuguese 😉)
So it happened again. After some random event like a system upgrade or power failure(?), what I see upon booting, somehow, is Windows Boot Manager instead of GRUB. Here's how I fixed it.
Just sharing the OpenMSX settings I am using in order to properly use an Xbox 360 controller. The mappings are optimized for Konami games (in particular, Metal Gear 2: Solid Snake).
I've been watching and toying a little bit with the Godot game engine for some time now, and it impressed me in several ways. One thing I missed, however, was a satisfying way to write more efficient code in those ever rarer situations in which GDScript1 couldn't give the speed I wanted. I could write a module in C++, but this involved recompiling the whole engine and, well, programming in C++.
Now, with the upcoming Godot 3.0 (currently in alpha), a much nicer alternative has been introduced: GDNative. I tested it and it mostly worked. Here's a summary of my experience.
On the second and final part of this conceptual introduction to Machine Learning (ML), I'll discuss its relationship with other areas (like Data Science) and describe what I perceive as a common theme among many of the ML algorithms. Emphasis on “what I perceive”: don't take this as the truth.
“Machine Learning” is not just a buzzword — arguably, it is two. Almost everybody seems to be using Machine Learning (ML) in a way or another, and those who aren't are looking forward to use it. Sounds like a good topic to know about. I did some nice Neural Network stuff with some colleagues in school in the late 90s1. Maybe I could just brag that I have nearly 20 years of experience in the field, but this would not be exactly an honest statement, as I didn't do much ML since then.
Anyway, this is a fun, useful and increasingly important field, so, I guess it is time to do some ML for real. Here's the first set of notes about my studies, in which I present some important concepts without getting into specific algorithms.
Once upon a time, Peter John Acklam devised a nice algorithm to approximate the quantile function (AKA inverse cumulative distribution function, or inverse CDF) of the normal distribution. He made the algorithm freely available, but unfortunately his page describing it has been timing out for quite a while. So, for reference, here's a quick overview of his algorithm.