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Hello!

Abstract: in this post I discuss clustering: techniques that form this method and some peculiarities of using clustering in practice. This post continues previous one about the OPERA.

The Commands

My latest work titled “Disinformation and social bot operations in the run up to the 2017 French presidential election” investigates the #MacronLeaks disinformation campaign that occurred in the run up to the 2017 French presidential election.

A couple of months ago we (Kevin Scaman, Francis Bach, Yin Tat Lee, Laurent Massoulie and myself) uploaded a new paper on distributed convex optimization. We came up with a pretty clean picture for the optimal oracle complexity of this setting, which I will describe below. I should note that there are hundreds of papers on this topic, but the point of the post is to show our simple cute calculations and not to survey the immense literature on distributed optimization, see the paper itself for a number of pointers to other recent works.

We recently released PyMC3 3.1 after the first stable 3.0 release in January 2017. You can update either via pip install pymc3 or via conda install -c conda-forge pymc3.

Kaggle are running a regression machine learning competition with Mercedes-Benz right now, it closes in a week and runs for about 6 weeks overall. I’ve managed to squeeze in 5 days to have a play (I managed about 10 days on the previous Quora competition). My goal this time was to focus on new tools that make it faster to get to ‘pretty good’ ML solutions. Specifically I wanted to play with:

Are you a Python Hero and do you want to learn more about Java? Then continue reading! I will discuss issues I’ve seen from a Pythonic viewpoint when I was learning Java. I will also talk about why Java is preferred over Python in some cases.

In late 2016, Ben Lorica of O’Reilly Media declared that “2017 will be the year the data science and big data community engage with AI technologies.” Deep learning on GPUs has pervaded universities and research organizations prior to 2017, but *distributed deep learning on CPUs *is now beginning to gain widespread adoption in a diverse set of companies and domains. While GPUs provide top-of-the-line performance in numerical computing, CPUs are also becoming more efficient and much of today’s existing hardware already has CPU computing power available in bulk. The emergence of open source tools like deeplearning4j, which bring fast deep learning at scale to the Hadoop stack, will be major catalysts to the impact of deep learning in the coming years.