Launch Stack on AWS (Costs Money!)

It looks complicated at first but, actually, we don’t need to worry about angles, we can simply work with symmetry. The hexagon contains six equilateral triangles (shown in red below). Each of these triangles is 1/6 of the area of the large hexagon.

Abstract: in this post I explain how Machine Learning tools can be applied to particle physics. I’ll discuss what is a particle shower, and when it appears in the OPERA and how clustering and classification techniques can help fundamental research.

In 2013, I was an embedded team member at IDEO during an externship swap. I was there to help IDEO think about how they could use data for prototyping during a project. To begin, the IDEO team performed some qualitative research to learn what consumers needed and wanted, then we ran a quantitative survey online. As the results trickled in, we started to analyze the data.

Reddit is a popular social news aggregator and discussion site with hundreds of thousands of subreddits devoted to every topic one can imagine. One special kind of subreddits are the “Ask” forums where questions are posed and answered among subscribers. A particularly interesting subreddit is AskScience, where questions from various scientific disciplines are discussed in a very informed manner, giving a lot of references and different angles on a topic. We wanted to take this data as an opportunity to develop a workflow for deriving datasets from Reddit and to analyse them using the Lateral Intelligence Platform LIP.

Hey, remember when I wrote those ungodly long posts about matrix factorization chock-full of gory math? Good news! You can forget it all. We have now entered the Era of Deep Learning, and automatic differentiation shall be our guiding light.

TLDR: I’ve recently wondered about whether the Universe is truly random, and I thought I’d write down a few thoughts on the subject. As a heads up, this post is more about sharing a personal journey I’m on than teaching a skill or tool (unlike many other blogposts). Feel free to chat with me about these ideas on Twitter as I’m still working through them myself and I’d love to hear your perspective.

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As I move through my 20’s I’m consistently delighted by the subtle ways in which I’ve changed.

Bayesian probabilistic models provide a nimble and expressive framework for modeling “small-world” data. In contrast, deep learning offers a more rigid yet much more powerful framework for modeling data of massive size. Edward is a probabilistic programming library that bridges this gap: “black-box” variational inference enables us to fit extremely flexible Bayesian models to large-scale data. Furthermore, these models themselves may take advantage of classic deep-learning architectures of arbitrary complexity.