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Ruby On Ales 2016

This presentation, by Julia Ferraioli, is licensed under a Creative Commons Attribution ShareAlike 3.0
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One of the most common reasons for using machine learning is because you want to use data to make sense of more data: given lots of things with many characteristics, how do you use them to categorize, make recommendations, or evaluate something new? We can use machine learning for all sorts of lofty goals, so let’s tackle a critical problem in our lives: whiskey. I’m a novice to whiskey, and so far I’ve stuck to the lighter stuff. Finding new ones to add to my collection has been decidedly unscientific. Given the data we have available about whiskey, let’s try doing a bit of machine learning by feeding it into TensorFlow, an open source library, to see what cool insights we can get into a complex spirit.

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