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. Only to realize I was going to be out of town the whole weekend.
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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 set up Travis-CI integration and spent some time on one of the issues that multiple people had reported.
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I'm at RecSys 2014, meeting a lot of people and hanging out at talks. Some of the discussions here was about the filter bubble which prompted me to formalize my own thoughts.
I firmly believe that it's the role of a system to respect the user's intent.
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Note: This is a silly application. Don't take anything seriously.
Benford's law describes a phenomenon where numbers in any data series will exhibit patterns in their first digit. For instance, if you took a list of the 1,000 longest rivers of Mongolia, or the average daily calorie consumption of mammals, or the wealth distribution of German soccer players, you will on average see that these numbers start with “1” about 30% of the time.
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Inspired by Sander Dieleman's internship at Spotify, I've been playing around with deep learning using Theano. Theano is this Python package that lets you define symbolic expressions (cool), does automatic differentiation (really cool), and compiles it down into bytecode to run on a CPU/GPU (super cool).
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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. The group with the highest conversion rate wins, right?
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I’ve been spending quite some time lately playing around with RNN’s for collaborative filtering. RNN’s are models that predict a sequence of something. The beauty is that this something can be anything really – as long as you can design an output gate with a proper loss function, you can model essentially anything.
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