Systemml – distributed and declarative machine learning platform. Looks like a promising project which now joined Apache Software foundation and was initially developed by IBM. I wonder how it will influence the development of Spark MLLib.
Monopoly as Markov Chain – I guess I am developing a fetish to Markov chains. Although this model is not always realistic (so as in this case..) it is amazing what we can get out of it and the approximate simulations we can create. This post simulates a Monopoly game using Markov chains and does a very interesting job. Monopoly is linear in the sense that you must move according to the dice and the choices you make are limited (buy or don’t buy). In contrast to backgammon where you also have strategy involved and therefore it is a better choice to model it with Markov chains.
http://koaning.io/monopoly-simulations.html
Vocabulary – “Python Module to get Meanings, Synonyms and what not for a given word”. This module brands itself as an alternative to NLTK presenting data about meaning, synonyms, antonyms, part of speech, pronunciation, etc with a leaner approach and more pythonic approach. I don’t know if is as good and NLTK or will evolve there but it sure looks like an alternative worth checking.
http://vocabulary.readthedocs.org/en/latest/
Probability recap – If you forgot probability class from university this will probably be a good recap. However, if you work as a data scientist you probably used those daily. But code visualization, good examples and good way to share your knowledge with other colleagues.
What we talk about when we talk about distributed systems – for once a non misleading title. Thinking about the distributed systems course I took on grad school this would have been a great introduction.
http://videlalvaro.github.io/2015/12/learning-about-distributed-systems.html
Tom Ron 