Home on Erik Bernhardsson

I was browsing around on the Internet and the physics geek in me started reading about Fermat’s principle.

Just promoting Spotify stuff here: check out the Snakebite repo on Github, written by Wouter de Bie.

The simple way to get featured on big data blog these days seem to be Build something that does 1 thing super well but nothing else Benchmark it against Hadoop Publish stats showing that it’s 100x faster than Hadoop $$$ Spark claims their 100x faster than…

I like the editing!

I picked up an issue of Foreign Affairs while flying back to NYC from SFO.

Annoy is a simple package to find approximate nearest neighbors (ANN) that I just put on Github.

Elias Freider just talked about Luigi at PyData 2013: The presentation above is much better than one I put together a few weeks ago.

I recently came across this paper describing how they do ML at Twitter.

This article from today in Mashable describes some of the fun stuff I get to work with: Erik Bernhardsson is technical lead at Spotify, where he helped to build a music recommendation system based on large-scale machine learning algorithms, mainly matrix…

Slides from the talk.

From the NYC Machine Learning talk I had last week: Haven’t looked at it yet except briefly.

The Economist just published an article called The best, the worst and the ugly.

This was posted on the Twitter Engineering blog a few days ago: Dimension Independent Similarity Computation (DISCO) I just glanced at the paper, and there’s some cool stuff going on from a theoretical perspective.

Not sure how I managed to miss this, but I’m watching this Tumblr presentation and they talk about their projects named after Arrested Development topics: Gob, Parmesan, Buster, Jetpants, Oscar, George and Motherboy.

Something that pops up pretty frequently is to implement time decay, especially where you have recursive chains of jobs.

I’m shamelessly promoting my first major open source project.