Blog Posts by Andrew B. Collier / @datawookie

I followed up a reference to fast-neural-style from Twitter and spent a glorious hour experimenting with this code. Very cool stuff indeed. It’s documented in Perceptual Losses for Real-Time Style Transfer and Super-Resolution by Justin Johnson, Alexandre Alahi and Fei-Fei Li.

The basic idea is to use feed-forward convolutional neural networks to generate image transformations. The networks are trained using perceptual loss functions and effectively apply style transfer.

What is “style transfer”? You’ll see in a moment.

I’m generally not too interested in fitting analytical distributions to my data. With large enough samples (which I am normally fortunate enough to have!) I can safely assume normality for most statistics of interest.

Recently I had a relatively small chunk of data and finding a decent analytical approximation was important. I had a look at the tools available in R for addressing this problem. The {fitdistrplus} package seemed like a good option. Here’s a sample workflow.

I’m busy working my way through Kyle Banker’s MongoDB in Action. Much of the example code in the book is given in Ruby. Despite the fact that I’d love to learn more about Ruby, for the moment it makes more sense for me to follow along with Python.

Sometimes you’ll want to see how a site behaves on a slower connection. This can be easily emulated using Chrome DevTools. Go to the Network tab and press the “No throttling” dropdown, which will give you a selection of presets and the option to configure custom connections.

When figuring out how to formulate the contents of a POST request it’s often useful to see the “typical” fields submitted directly from a web form.

Seems that I am doing this a lot: deleting my entire graph (all nodes and relationships) and rebuilding from scratch. I guess that this is part of the learning process.

Accessing the Neo4j server running on your local machine is simple: just point your browser to http://localhost:7474/. But with the default configuration the server is not accessible from other machines. This means that other folk can share in the wonder of your nodes edges.

Some instructions for installing Neo4j on Ubuntu 16.04. More for my own benefit than anything else.

In a previous post I detailed the process of compiling data on subject keywords used in articles published in PLOS journals. In this instalment I’ll be using those data to mine Association Rules with the arules package.

Uber exposes an extensive API for interacting with their service. ubeR is a R package for working with that API which Arthur Wu and I put together during a Hackathon at iXperience.

I’m putting together a couple of articles on Collaborative Filtering and Association Rules. Naturally, the first step is finding suitable data for illustrative purposes.

In previous instalments in this series I have not taken into account how odds can change over time.

I finally got tired of booting up Windows to download data from my Garmin 910XT. I tried to get my old Ubuntu 15.04 system to recognise my ANT stick but failed. Now that I have a stable Ubuntu 16.04 system the time seems ripe.

In the first instalment of this series we gained an understanding of the various types of odds used in Sportsbook betting and the link between those odds and implied probabilities. We noted that the implied probabilities for all possible outcomes in an event may sum to more than 100%. At first sight these seems a bit odd. It certainly appears to violate the basic principles of statistics. However, this anomaly is the mechanism by which bookmakers assure their profits. A similar principle applies in a casino.

I’m working on a project in which I need to systematically parse a number of RSS and Atom feeds from within R. I was somewhat surprised to find that no package currently exists on CRAN to handle this task. This presented the opportunity for a bit of DIY.

You can find the fruits of my morning’s labour here.

A couple of my collaborators have had trouble using read_html() from the xml2 package to access this Wikipedia page.

This series of articles was written as support material for Statistics exercises in a course that I’m teaching for iXperience. In the series I’ll be using illustrative examples for wagering on a variety of Sportsbook events including Horse Racing, Rugby and Tennis. The same principles can be applied across essentially all betting markets.

Arthur Benjamin thinks that the end goal of teaching Mathematics at school should be Statistics rather than Calculus. He has a point: in terms of understanding things in the real world, Statistics is definitely more powerful. These ideas are quite compatible with those of Conrad Wolfram, who thinks that we should be using computers more extensively in Mathematics education.