Is that a baby bump in your status? Janet Vertesi tried to keep her pregnancy a way from the social networks. Did she succeed? What was the price? (high)
http://time.com/83200/privacy-internet-big-data-opt-out/
Spurious Correlation – “marriages in Alabama are causing deaths by electrocution” – the nightmare of every data scientist. Are two variable correlated? Is one the cause of the other or the other is the cause of the one? This post took it one step forward –
History of Machine Learning – Another way to view machine learning and the progress made over the years. I’m wondering about the exact starting point \ initial formation of machine learning, I think it is a bit earlier than what is stated there. The reason to read this article is given in Pirkei Avot: “Know from where you came and where you are going and before whom you are destined to give account and reckoning” (3;1).
www.erogol.com/brief-history-machine-learning/
Breaking Python 2 for 1 – I have missed the first post when it was published so got 2 for 1 now. Two fascinating blog posts about under the hood of CPython, ctypes, garbage collector, etc. I really appreciate the hands on approach, it reveals a side of Python which I’m not exposed to on my daily work (and probably for most projects it is not a good practice to mess with).
http://blog.hakril.net/articles/0-understanding-python-by-breaking-it.html
http://blog.hakril.net/articles/1-understanding-python-by-breaking-it—lvl-2.html
Where is Waldo? – find Waldo in just 42 lines of code (not surprising having that 42 is the meaning of life). I have not background in image processing but those tools really make it easy, almost out of the box (and great flashback to my childhood).
http://machinelearningmastery.com/using-opencv-python-and-template-matching-to-play-wheres-waldo/
Tom Ron 