1. Demystifying Logistic Regression

    For our hackathon this week, I, along with several co-workers, decided to re-implement Vowpal Wabbit (aka "VW") in Go as a chance to learn more about how logistic regression, a common machine learning approach, works, and to gain some practical programming experience with Go.

    Though our hackathon project focused on learning Go, in this post I want to spotlight logistic regression, which is far simpler in practice than I had previously thought. I'll use a very simple (perhaps simplistic?) implementation in pure Python to explain how to train and use a logistic regression model.




  2. Optimize Python with Closures

    Magnetic's real-time bidding system, written in pure Python, needs to keep up with a tremendous volume of incoming requests. On an ordinary weekday, our application handles about 300,000 requests per second at peak volumes, and responds in under 10 milliseconds. It should be obvious that at this scale optimizing the performance of the hottest sections of our code is of utmost importance. This is the story of the evolution of one such hot section over several performance-improving revisions.


  3. Good Test, Bad Test

    A good test suite is a developer's best friend -- it tells you what your code does and what it's supposed to do. It's your second set of eyes as you're working, and your safety net before you go to production.

    By contrast, a bad test suite stands in the way of progress -- whenever you make a small change, suddenly fifty tests are failing, and it's not clear how or why the cases are related to your change.


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