My colleagues Austin Taylor and Melissa Kilby are proud to announce the first stable release of Griffon: A Virtual Machine for Data Science. Griffon is a virtual machine which contains many data science tools pre-configured, installed and linked up to make it so that you don’t have to be a Linux expert to try them out. If you are teaching a class, or if you are simply wanting to learn more about a particular tool, then Griffon is perfect for you.

I am currently working on my master thesis in meteorology and got into machine learning last year. I have roughly 1500 cases of forecasts by a weather model (a lot of parameters ~ 400) with the respective "solution" (the measurements). I turned this into a classification forecast by assigning wind gusts <= 25 knots to class "0" and the others to class "1" because it has tradition to just report wind gusts above 25 knots in meteorological ground measurements. My current thoughts are:

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This post presents a summary of a series of tutorials covering the exercises from Andrew Ng's machine learning class on Coursera. Instead of implementing the exercises in Octave, the author has opted to do so in Python, and provide commentary along the way.

Last week Open Data Science published an article about how I plan to teach myself deep learning using only free resources and after my first week I’m here to report on my progress and take on the resources I’ve used so far. This piece is specifically about video learning content. I purposefully decided to start off with video because I wanted to ease myself into this incredibly complex subject, the same way a swimmer enters a chilly body of water.

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Also AI and Deep Learning, Explained Simply; When not to use deep learning; Optimism for AI drop with experience developing AI systems.

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