The hidden bugs waiting to be found by your international users
While internationalizing our applications, we focus on the things we can see: text, tool-tips, error messages, and the like. But, hidden in our code there are places requiring internationalization that tend to be missed until found by our international users and reported as a bug.
Here’s a big one: regular expressions. You likely use these handy, flexible, programming features to parse text entered by users. If your regular expressions are not internationalized, more specifically, if they are not written to handle Unicode characters, they will fail in subtle ways.
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Linear Regression on a huge dataset made easy with Apache Spark running on AWS
The final project for the Udacity Data Science Nanodegree program involved learning and using the Apache Spark platform to train a machine learning model on a large dataset. For the previous projects in this program, we were able to download the entire dataset on a local machine and do the exploration, cleaning, and engineering. In this case, the dataset was over 12G and could not be loaded onto a typical development machine. The Spark platform makes it relatively easy to handle these large datasets and reduce the time it takes to train models.
What follows is my step by step…
Data science helps demystify the language
Enjoying wine is one thing, understanding the adjective laden language experts use when talking about it is another. If you’re like me, you’ve been amused and confused by descriptions comparing the taste (approvingly!) to “wet granite” or discussing the “nose” having hints of “graphite and pencil shavings”. Throw in terms like tannins, acidity, and finish and the casual wine drinker is left wondering what the description is telling them about the wine.
Is there something we can decipher from all the descriptive terms? …
Ken Richards
Software Engineer and Senior Team Lead @ JamaSoftware, Jazz Player, Poker Fanatic
