Implement A Distributed Math algorithm in 2hrs: K-Means

We will take a simple yet popular & powerful math algorithm such as K-Means and implement a distributed version in 2hrs.

 

Pre-requisites: Knowledge of Java

K-Means: http://en.wikipedia.org/wiki/K-means_clustering

For extra credit -

We'll look at KNN (K-Nearest Neighbors) https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm

 

See: http://h2o.0xdata.com/

 

 




 

 

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  • Stephen

    My Meetup calendar did not display this RSVP so I missed it. Grr!

    June 21, 2013

  • srisatish

    WARNING: Only software programmers ignore warnings! :)

    That said, this seriously is a very hands on java-intense exercise. Extinguished engineers will not enjoy the proceedings this thursday.

    Patrons and participants need to ensure that the following steps have been achieved before hand:

    1. Downloaded the H2O source from: https://github.com/0xdata/h2o
    like so,

    $ git clone https://github.com/0xdata/h2o.git

    2. Installed IntelliJ or eclipse on your laptop.

    3. Imported project into IDE (by pointing to h2o directory)

    4. Run main in water.Boot within IDE
    (This should launch H2O within IDE)

    This session will be hands-on: Hacking & less of a spectator sport.

    This session will expose user to concepts of Distributed Fork / Join and K/V Store and Math-hacking over simplest Map & Reduce concepts, as well as DRemoteTask.

    Finally, the v2 of API is under development & some of the boilerplate will go away & get a bit easier in time. Hoping to see the hacker in you this thursday!

    June 18, 2013

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