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During graduate school, I was exposed to the power of CUDA cores through my parallel computing class.  Back then, there was a relatively small number of such cores on the video card inside their shared server, something like 40 if I remember correctly.  With my NVIDIA GeForce GTX 650 Ti video card, however, I now have 768 CUDA cores at my disposal -- almost 20 times as many as in grad class 4 years ago! Not being much of a mathematician at heart, and generally spending time on logic problems, application testing, or new HTML5 & browser paradigms rather than crunching big data, I was never really inspired to do much with these cores.  This all changed when watching the Google I/O 2015 keynote address when they showed off the capability for you to draw (as best you can) an emoji, and Google's engine will try to recognize your scrawl and offer you up several profesionally-drawn emojis to represent whatever it is you're trying to express.  With recent changes in my...

Another cool project I did back in college was to create a classifier that could look at a digital photograph or video and discern where there was human skin.  Once you know where the skin is, you can analyze the shape of it, or treat it like a lamina and find its "center of mass" and how it changes over time in a video.  However, before you can start thinking about scanning for people and interacting with their motion (or whatever your application may be), you must first learn how to collect and interpret the raw data. Skin in an image is found by analyzing the colors.  Digital images exist in a "color space" which is a mapping of each distinct color to a particular point on a graph whose axes relate to very basic properties of colors.  The most common color space is RGB -- each color can be defined by the percentage of red, green, and blue it contains.  (Well actually, there are some colors that can't be represented in the RGB space, but most cameras can...