Zacstewart.com Website Review

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 scra*e by in this hostile environment was to let Rails’ as*et 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. Docu*ent clas*ification is a fundamental machine learning task. It is used for all kinds of applications, like filtering spam, routing genre clas*ification, sentiment an*lysis, and many more. To demonstrate text clas*ification 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. 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 is a moderately detailed explanation and a few examples of how I use pipelining when I work on competitions.