The -server problem is a classical and very attractive instance of online decision making. The decisions to be made in this problem are simple: given a requested location in some finite metric space and a fleet of k servers currently sitting in this metric space, one has to choose which server to use to service the request (that is the server will move from its current location to the requested location). The problem is to minimize the total amount of movement of the servers on a stream of such requests. This formulation is both abstract enough that it can model many real-life problems (e.g., virtual memory management) yet concrete/simple enough that it seems that everything ought to be understood about it.

Hebbian Learning in Neural Networks

Here are the rules: if you read this post all the way through, you have to deploy a smart contract on your private Ethereum blockchain yourself. I give you all the code I used here in Github so you have no excuses not to.

Lately I’ve read a lot of attempts at defining data scientist and differentiating it from other data-centric roles. The terms ‘data scientist’, ‘data analyst’, and ‘data engineer’ are obviously interrelated. But recently I’ve seen some weird definitions of them.

Are youJava Developer and eager to learn more about Deep Learning and his applications, but you are not feeling like learning another language at the moment ? Are you facing lack of the support or confusion with Machine Learning and Java?

TLDR: I review a recent systems paper from Google, why it is a wake-up call to the industry, and the recipe it provides for nonlinear product thinking.

Before I get to the videos, I wanted to say a few words about when and why you might choose Cython.

In a previous post, I looked at how the established value based drafting (VBD) algorithm for picking fantasy football rosters would perform in a league of typical human players.  It turned out that we get different performance depending on if we look at ranks of VBD drafters based (i) on expected preseason player forecasts or (ii) on actual points scored by a player that season.  Based on preseason forecasts, we could expect a VBD roster to place 2nd in a 12 player league, while using actual player points, VBD is only expected to rank at 4.68.  That’s still better than it would do by chance (if VBD came in 6th place), but it’s really only a slight advantage.  This made me wonder if it would be all that difficult to improve on VBD using methods similar to those used to train AIs to play video/board games.

The Mobility Robotics course is finally done, and I just started Perception. It seems to be way more concept-heavy than any of the other courses, but I like the content from Week 1 so far! I did not like Mobility as much, since it focussed exclusively on theory, and the content assumed a fair amount of comfort with kinematics/dynamics (which I don’t have anymore). Anyway, off to the articles for this week: