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How do you know what machine learning algorithm to choose for your classification problem? Of course, if you really care about accuracy, your best bet is to test out a couple different ones (making sure to try different parameters within each algorithm as well), and select the best one by cross-validation. But if you’re simply looking for a “good enough” algorithm for your problem, or a place to s

I built a visualization to explore embeddings a few years ago, but never posted it more broadly. So here it is! http://blog.echen.me/embedding-explorer/ These are GloVe embeddings projected into 2D, colorized via k-means in the original space. You can see, for example, that the cluster in pink at the bottom right is a cluster of names. And the cluster in red on the right is a cluster of geographic

This is a bare-bones introduction to ggplot2, a visualization package in R. It assumes no knowledge of R. For a better-looking version of this post, see this Github repository, which also contains some of the example datasets I use and a literate programming version of this tutorial. Preview Let’s start with a preview of what ggplot2 can do. Given Fisher’s iris data set and one simple command… 1 q

Scalding is an in-house MapReduce framework that Twitter recently open-sourced. Like Pig, it provides an abstraction on top of MapReduce that makes it easy to write big data jobs in a syntax that’s simple and concise. Unlike Pig, Scalding is written in pure Scala – which means all the power of Scala and the JVM is already built-in. No more UDFs, folks! This is going to be an in-your-face introduct

Imagine you have a sequence of snapshots from a day in Justin Bieber’s life, and you want to label each image with the activity it represents (eating, sleeping, driving, etc.). How can you do this? One way is to ignore the sequential nature of the snapshots, and build a per-image classifier. For example, given a month’s worth of labeled snapshots, you might learn that dark images taken at 6am tend