I spent the last hour thinking and creating an example that illustrates my question: https://imgur.com/a/36gU0pb My question indirectly relates to exploratory data analytics and feature selection for statistical models. Suppose you have some variables (let's assume they are categorical variables for this example) - when you make a histogram for these variables, they appear extremely skewed. On first glance, you would not want to include heavily skewed variables as inputs for a statistical model - e.g., if 99% of the variable is a single value, how informative and useful could it be to a statistical model? But how do you know that these heavily skewed variables don't contain some very useful information in the 1%, that might really help you in making future predictions?
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Remember all those beautiful photos taken on the iPhone 7 and the iPhone 7 Plus that Apple showed during its September keynote? Turns out, the company has many more photos and videos it uses for showcase purposes in Apple Stores -- and now you can download them all. One clever Redditor noticed (and 9to5Mac wrote up) that you can simply download all these photos and videos by using Apple's AirDrop near an Apple Store, and connecting to one of the devices there. He collected a total of 195 images, which he shared on Imgur. Since Imgur compresses the photos, therefore impacting the quality, the same user also shared the entire stash of photos and videos, in full resolution, on Dropbox (unfortunately, before the publication of this article Dropbox temporarily terminated te account due to too much traffic).
Ray Kurzweil's predictions have an 86% success rate. But what he sees in the next 50 years are mind-blowing. Check out Ray's books for more about his predictions for the future: The Age of Intelligent Machines http://amzn.to/1UMU4YM The Singularity is Near http://amzn.to/1UMUfn1 Follow me at answerswithjoe on: Facebook Twitter Instagram Snapchat Ray Kurzweil is an inventor and futurist who has championed the Law of Accelerating Returns, claiming that computer technology is following an exponential path that will lead to the Singularity - a point in time when computer power reaches super intelligence and all things are possible.
I tried a fun little project to explore some of the possibilities of GANs and the results came out pretty good so I figured I'd share. The goal was to create a network which can take extremely simple images composed of various color regions (where each color corresponds to a particular type of terrain) and generate a realistic looking terrain map.