Posts

Spinning up PostgreSQL in Docker for Easy Analysis

My typical analysis workflow is to start with data in some kind of database, perhaps Redshift or Snowflake. Often I’m working with millions or even billions of rows, but modern databases excel at operating with data at scale.
Jul 25, 2020 4 min read

Timekeeping with Emacs and Org-Mode

Although I have been an Emacs user for 15 years, for the first 13 of those years I only used a handful of commands and one or two “modes”. A couple years ago I went through the Emacs tutorial (within Emacs, type C-h r) to see if I was missing anything useful.
Jul 6, 2020 5 min read

A/B Testing Best Practices

When I started this blog, my primary objective was less about teaching others A/B testing and more about clarifying my own thoughts on A/B testing. I had been running A/B tests for about a year, and I was starting to feel uncomfortable with some of the standard methodologies.
Jun 18, 2020 9 min read

Getting Things Done

Getting Things Done or GTD is a productivity framework introduced by David Allen. Since his book was first published in 2001, the paradigm has achieved something of a cult status, especially among Emacs users. In this post I will describe my very-much-in-progress implementation of these systems.
Jun 4, 2020 8 min read

Object Detection with Deep Learning

One of the most interesting topics in the Coursera Deep Learning specialization is the “YOLO” algorithm for object detection. I often find it helpful to describe algorithms in my own words to solidify my understanding, and that is precisely what I will do here.
Jun 2, 2020 7 min read

Thoughts on the Coursera Deep Learning Specialization

I recently completed the Deep Learning specialization on Coursera from deeplearning.ai. Over five courses, they go over generic neural networks, regularization, convolutional neural nets, and recurrent neural nets. Having completed it, I would say the specialization is a great overview, and a jumping off point for learning more about particular techniques.
May 22, 2020 2 min read

Distribution of Local Minima in Deep Neural Networks

The “unreasonable effectiveness of deep learning” has been much discussed. Namely, as the cost function is non-convex, any optimization procedure will in general find a local, non-global, minimum. Actually, algorithms like gradient descent will terminate (perhaps because of early stopping) before even reaching a local minimum.
May 18, 2020 5 min read

Computer Vision Cheat Sheet

I am currently working through Convolutional Neural Networks, the fourth course in the Coursera specialization on Deep Learning. The first week of that course contains some hard-to-remember equations about filter sizes and padding and striding and I thought it would be helpful for me to write it out for future reference.
May 15, 2020 2 min read

Deep Learning Checklist

Recently I started the Deep Learning Specialization on Coursera. While I studied neural networks in my masters program (from Andrew Ng himself!), that was a long time ago and the field has changed considerably since then.
May 14, 2020 8 min read

Repeatability

As businesses continue to invest in data-driven decision making, it becomes increasingly important to ensure the methods underlying those decisions are reliable. Unfortunately, we cannot take this for granted! Read on to learn a collection of best practices to make sure your decision making process rests on a sturdy foundation.
May 13, 2020 3 min read