Home on Erik Bernhardsson

Wow I guess it was more than a year ago that I tweeted this.

For most people straight out of school, work life is a bit of a culture shock.

Febrary 6 was my last day at Spotify.

Chris Johnson‘s presentation from Data Day Texas:

I just made it to Sweden suffering from jet lag induced insomnia, but this blog post will not cover that.

Just search for “hackers gif“.

I was talking with some data engineers at Spotify and had a moment of nostalgia. 2008 I was writing my master’s thesis at Spotify and had to run a Hadoop job to extract some data from the logs.

More Luigi presentations!

At NYC Data Science meetup!

This is the last post about deep learning for chess/go/whatever.

My previous blog post about deep learning for chess blew up and made it to Hacker News and a couple of other places.

I’ve been meaning to learn Theano for a while and I’ve also wanted to build a chess AI at some point.

Say you build a machine learning model, like a movie recommender system.

I keep forgetting to buy a costume for Halloween every year, so this year I prepared and got myself a Luigi costume a month in advance.

I spent a couple of hours this weekend going through some pull requests and issues to Annoy, which is an open source C++/Python library for Approximate Nearest Neighbor search.

I’m at RecSys 2014, meeting a lot of people and hanging out at talks.

Note: This is a silly application.

Inspired by Sander Dieleman’s internship at Spotify, I’ve been playing around with deep learning using Theano.

Many years ago, I used to think that A/B tests were foolproof and all you need to do is compare the metrics for the two groups.

I’ve been spending quite some time lately playing around with RNN’s for collaborative filtering.

One obvious thing to anyone living in NYC is how tourists cluster in certain areas.

During my time at Spotify, I’ve reviewed thousands of resumes and interviewed hundreds of people.

From my presentation at MLConf, one of the points I think is worth stressing again is how extremely well combining different algorithms works.

Just spent a day at MLConf where I was talking about how we do music recommendations.

Scrolling through the Discover page on Spotify the other day it occurred to me that the album is in fact a fairly strong visual proxy for what kind of content you can expect from it.

In case you missed it, we just acquired a company called Echo Nest in Boston.

So Luigi, our open sourced workflow engine in Python, just recently passed 1,000 stars on Github, then shortly after passed mrjob as (I think) the most popular Python package to do Hadoop stuff.

Haven’t posted anything in ages, so here’s a quick hack I threw together in Python on a Sunday night.

We run a ton of A/B tests at Spotify and we look at a ton of metrics.

Radim Rehurek has put together an excellent summary of approximate nearest neighbor libraries in Python.

I wanted to share some more insight into the algorithms we use at Spotify.

I think it’s funny how MS at some point realized they are not the cool kids and there’s no reason to appeal to that target audience.

One thing I encountered today was a trick using bagging as a way to go beyond a point estimate and get an approximation for the full distribution.

A lot of people have asked me what models we use for recommendations at Spotify so I wanted to share some insights.

Btw I just put something up online that I spent a couple of evenings in my couch putting together: it’s a website where you can track any numerical data on the web.

A lot of people these days know about collaborative filtering.

If you have a few minutes, you should check out mine and Chris Johnson‘s panel proposal.

I just answered a Quora question about what, if any, are the differences in the algorithms that are behind recommendations for music and movies.

Andy Sloane decided to call my 2D visualization and raise it to 3D.

I’m at KDD in Chicago for a few days.

I’ve turned into a lazy bastard and I’m just posting presentations on this blog, but here’s one from Rohan Singh at Spotify talking about the backend infrastructure of the Discover page.

I was just at the NYC Predictive Analytics meetup talking about how we build machine learning algorithms using Hadoop to power music recommendations.

I thought this article about the company culture at HubSpot is kind of funny.

I was in Portland, OR for a few days hanging out at OSCON.

Sometimes you have to maximize some function $$ f(w_1, w_2, ldots, w_n) $$ where $$ w_1 + w_2 + ldots + w_n = 1 $$ and $$ 0 le w_i le 1 $$ .

Continuing in the same spirit of shameless self-promotion, here’s some recent Luigi press: Reddit thread A Guide to Python Frameworks for Hadoop (slides from the NYC Hadoop User Group) This presentation from the Open Analytics NYC meetup about how Foursqu…

Just open sourced hdfs2cass which is a Hadoop job (written in Java) to do efficient Cassandra bulkloading.

We had an unconference at Spotify last Thursday and I added a semi-trolling semi-serious topic about abolishing documentation.

I’ve been obsessed with Wikipedia for the past ten years.

The Discovery page, the new start page in Spotify, is finally out to a fairly significant percentage of all users.