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Ever since 2012 I’ve had supported comments on this site using Disqus. Usually, the things I post don’t warrant a lot of discussion, but a few posts have garnered quite a few comments. My most popular post, Using scikit-learn Pipelines and FeatureUnions has 88 as of the time time I am writing this.
I have this Ruby project that I haven’t worked on in while. Over the last four or five years I’ve barely committed any work, just enough to keep it humming along. It has actual users (not that many though) and has hundreds of pages indexed by search engines. It’s just an open source community thing, not super important. I’ve been so lazy with it that a feature branch that I never merged into master has been running in production for years. Since it’s Saturday and I don’t have anything to do (thanks global pandemic), I decided to rectify that and finally clean this project up.
A well-known limitation of the iOS Simulator is that you are unable to test code that uses the camera. Unlike some other hardware features (Location Services, Touch ID, orientation, gestures), Apple has yet to add a way to either link the Simulator’s camera output to a camera on the host device, or even allow you to choose a static image to “mock” the input of the camera. That’s the goal of this tutorial. I’ll walk you through creating an interface around the camera and then mocking it to use on the Simulator. The result will be that you can include some static images to represent the front and rear cameras when running your app in the Simulator.
While working on a project recently, I encountered a problem I haven’t had to tangle with in a while: authenticating front-end applications against a Rails API. The last time I was even dabbling in this realm, jQuery was everything, CORS was still in its infancy, and JSONP was still a thing (that’s not a thing anymore, right?). The only way I ever managed to scrape by in this hostile environment was to let Rails’ asset pipeline serve up the front-end app and rely on same-origin requests and regular ol’ cookies to handle authentication. I didn’t like it, but I survived. Eventually, I got away from front-end concerns almost completely.
Document classification is a fundamental machine learning task. It is used for all kinds of applications, like filtering spam, routing support request to the right support rep, language detection, genre classification, sentiment analysis, and many more. To demonstrate text classification with scikit-learn, we’re going to build a simple spam filter. While the filters in production for services like Gmail are vastly more sophisticated, the model we’ll have by the end of this tutorial is effective, and surprisingly accurate.
Since I posted a postmortem of my entry to Kaggle’s See Click Fix competition, I’ve meant to keep sharing things that I learn as I improve my machine learning skills. One that I’ve been meaning to share is scikit-learn’s pipeline module. The following is a moderately detailed explanation and a few examples of how I use pipelining when I work on competitions.
The purpose of this post isn’t to bemoan the expanding surveillance state, warn of impending civil liberty revocation, or even to make you feel paranoid. I only want to talk sensibly about a few tools that we should all be comfortable using and know when we should use them.
I read a total of 14 books last year. I had set my goal for 15, but finished the year two-thirds of the way into three different books. I tend to read plurally.
When I need to give my brain a rest, I like to play Minecraft on an interesting server known as Civcraft. The unique thing about this server is that it is an experiment in anarchy of sorts. There are no rules except not to exploit software glitches that could give you an unfair advantage. Robbery, murder, griefing and trolling of all sorts are completely legal within the rules of the server. As a result, there have evolved complex and organic societies complete with competing cities, marketplaces and even ad hoc police forces and bounty hunters.
I gave a talk to the Atlanta Ruby Users Group on using the HTTP OPTIONS method as outlined in my previous post. The slides aren’t super great without me talking to explain them, but they may be of some use.
The OPTIONS method is a somewhat obscure part of the HTTP standard that could be used today with a strong impact on the interconnectedness of the interwebs while requiring minimal effort. It’s role is well defined in RFC2616, yet no web services that I can find are taking advantage of it.
I’ve been using CanCan for managing role-based authorization in Rstrnt, my restaurant management solution. CanCan is a very simple and easy-to-use authorization library that works out-of-the-box with Devise (and any other authentication system that provides a current_user method). However I had a use case that doesn’t seem to be documented on the project’s wiki.
jQuery Meow mimics Growl notications. It supports all jQuery events and you can bind it to various sources for message input making it ideal for form validation, Rails flash notices, or a replacement for the alert() box.
Enjoy this Fibonacci function until I get my site finished