Linear Interpolation (Interpolation, part I)

In a couple of different occasions I discussed the topic of interpolation, without really going into the details. Lately, I had to interpolate data and that got me interested (again) in interpolation; and I think I should share some of the things I learned.

In this first post (of a series), let us begin by the simplest interpolation of all (after constant interpolation): linear interpolation.

Wallpaper: Self-Contained

(Self-Contained, 1920×1200)

Wallpaper: Spiral

(Spiral, 1920×1200)

Ambiguous Domain Names

Two weeks ago I attended the Hackreduce Hackathon at Notman House to learn about Hadoop. I joined a few people I knew (and some I just met) to work on a project where the goal was to extract images from the Wikipedia and see if we could correlate the popularity, as the number of references to the image, to some of the intrinsic images characteristics.

But two other guys I know (David and Ian) worked on a rather amusing problem: finding domain names that can be parsed in multiple, hilarious ways. I decided to redo their experiment, just for fun.

Faster Collatz

Quite a while ago, I presented the Collatz conjecture and I was then interested in the graphical representation of the problem—and not really going anywhere with it.

In this entry, let us have a look at the implementation of the Collatz function.

Introduction to Gradient Descent

In a previous post, I presented (generalized) linear regression from a linear algebra point of view (namely, the “normal equations”) and in this post, I discuss yet another take on the problem, this time using gradient descent.

Gradient descent isn’t particularly fascinating for this particular task (as we know closed, analytical expressions for obtaining the parameters), but linear regression is the simplest example of gradient descent I could come up with without being completely trivial.

New Way of Computing Square Roots?

I sometimes have quite nerdy readings. As of late, I’ve been reading Le fabuleux destin de (The Fabulous Destiny of , one might translate) by Benoît Rittaud. This book is a treasure trove of facts, and one caught my eye more than the others so far: an iterative method to compute square roots of any (positive) number.

When the method is first presented, he leaves to the reader to find a demonstration (though he gives one much later on, several chapters later), but let’s see what we can find.

Wallpaper: Je me souviens

(Je me souviens, 1920×1200)

In memory of the 1989 Polytechnique victims

Reblog if you’re in favor of saving the Canadian Firearm Registry.

Les Anciens

Contrairement à ce que croyaient les Anciens Grecs, le rôle de la science n’est pas de fignoler des représentations délectables, mais de prendre l’Univers comme elle le trouve.

Marcel Boll

Histoire des mathématiques

Herding Cats

I’m sure you’ve heard the expression herding cats before. When you’re trying to manage programmers, the expression certainly comes to mind. What if programmers were cats, which one would you be?