Wallets as a reward and penalty mechanism for online behavior
I’m creating an AI moderator for social media using reinforcement learning (RL). My goal is to reduce toxicity/outrage and increase …Continue reading →continue reading.
Author: Brian Lee Yung Rowe
Simulating Twitter users with reinforcement learning
I’m creating an AI moderator for social media using reinforcement learning. The goal is to reduce toxicity/outrage and increase civility …Continue reading →continue reading.
Lies, damned lies, and rankings: the problem with Bloomberg’s COVID resilience ranking
Every ranking creates winners and losers. In the case of Bloomberg’s Covid Resilience Ranking, the Philippines is a loser: dead …Continue reading →continue reading.
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 …Continue reading →continue reading.
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 …Continue reading →continue reading.
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 …Continue reading →continue reading.
Introducing the AI Workplace
My startup, Pez.AI, is launching a publication on Medium called the AI Workplace. We’re exploring a radically different way of …Continue reading →continue reading.
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 …Continue reading →continue reading.
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 …Continue reading →continue reading.
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 …Continue reading →continue reading.
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 …Continue reading →continue reading.
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 …Continue reading →continue reading.
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 …Continue reading →continue reading.
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 …Continue reading →continue reading.
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. …Continue reading →continue reading.
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 …Continue reading →continue reading.
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 …Continue reading →continue reading.
