How to call bullshit on AI companies (aka a short lesson on recall)
Now that software ate the world, what’s for dessert? Those in the know know that it’s AI. It seems everyone …
Category Archives: data science
Data-driven unit testing for data scientists and quant developers alike
Often overlooked, testing is a critical process that saves time over the long term and enables building complex systems. Unit …
Fermi Poker – Gambling for quants and data scientists
Fermi problems are well known for honing your ability to estimate quantities that are difficult (or impossible) to measure. Named …
Modeling data with functional programming, Part I
The latest draft of my book is available. This will be my last pre-publication update, as I’m in the process …
What you need to know about data augmentation for machine learning
Plentiful high-quality data is the key to great machine learning models. But good data doesn’t grow on trees, and that …
A simple workflow for deep learning
As a follow-up to my Primer On Universal Function Approximation with Deep Learning, I’ve created a project on Github that …
A primer on universal function approximation with deep learning (in Torch and R)
Arthur C. Clarke famously stated that “any sufficiently advanced technology is indistinguishable from magic.” No current technology embodies this statement …
Lead your own data science projects with the 3 Ps
There’s a lot of literature on learning the technical aspect of data science: statistics, machine learning, data munging, big data. …
Best practices for logging computational systems in R and Python
As is the case with most quant software, it’s a bit different from run-of-the-mill software. The somewhat prosaic world of …
How to write good tests in R
Testing is an often overlooked yet critical component of any software system. In some ways this is more true of …
