Articles tagged “python”

  1. Submit to PyGotham's CFP

    I'm honored to be an organizer on the program committee for PyGotham 2017 this year, and I encourage you all to submit a talk to our little conference. PyGotham will be held October 6, 7, and 8 in New York City, and the Call for Proposals is open until July 18.

    PyGotham attendees are diverse, come from varied backgrounds and skill levels, and have lives and interests beyond Python programming. Accordingly, the topics at PyGotham often vary a bit more widely than other programming language conferences. In the past, we have hosted talks about subjects from detecting sarcasm in audio files of speech, to open source stenography; from game programming to what we can learn about code review from J.R.R. Tolkien (sort of).

    The threads connecting all these talks together are Python and New York, and the people interested and involved in both of those. If you're a member of either of these communities, if you think the PyGotham audience would like to hear your talk, then we want to see your proposal

  2. Delete Your Dead Code!

    A few days ago, Ned Batchelder's post on deleting code made the rounds on HN, even though it was originally written in 2002. Here I want to echo a few of Ned's points, and take a stronger stance than he did: delete code as soon as you know you don't need it any more, no questions asked. I'll also offer some tips from the trenches for how to identify candidate dead code.

    This is the second in an ongoing series on eating your vegetables in software engineering, on good, healthy practices for a happy and successful codebase. I don't (yet) know how long the series will be, so please stay tuned!

  3. 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.

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