Machine Learning is really about mirroring data, and mirroring it in a way that sometimes approximates the thing that generates the data -- the real world process and its statistical footprints. If we make incorrect assumptions about the data, we are subject…Continue
Added by Pradyumna S. Upadrashta on January 7, 2017 at 2:30pm — No Comments
I randomly ran into this old thread of Vincent's in DSC regarding Moore's law and its applicability to how we might think about the growth of insights relative to the growth of our data. I took a humble crack at it. No idea if i'm…Continue
Throw them all out I say. Big Data is really just defined by one letter, D, Dimensionality.
(Or, at most, 2…Continue
Before jumping on the Big Data bandwagon, I think it is important to ask the question of whether the problem you have requires much data. That is, I think its important to determine when Big Data is relevant to the problem at hand.
The question of relevancy is important, for two reasons: (i) if the data are irrelevant, you can't draw appropriate conclusions (collecting more of the wrong data leads absolutely nowhere), (ii) the mismatch between the problem statement, the…Continue
Here is an interesting problem to play with in your down time. I will post the solution soon, when I get a moment to update this blog.
There are five houses in a row, each of a different color, that are inhabited by five people of different nationalities, with different pets, favorite drinks, and favorite sports. Use the clues below to determine who owns the monkey…