Slides for “Achieving Practical Reproducibility with Transparency and Accessibility” (DSSV 2020)
I was invited to speak at the ASA’s Symposium on Data Science and Statistics as well as the SAMSI/IASC conference …
Category Archives: data science
Request for comments on planned features for futile.logger 1.5
I will be pushing a new version of futile.logger (version 1.5) to CRAN in January. This version introduces a number …
Preview my new book: Introduction to Reproducible Science in R
I’m pleased to share Part I of my new book “Introduction to Reproducible Science in R“. The purpose of this …
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 …
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 …
