Introduction I’ve been looking for an all-in-one place to explain how to start from nothing to having a GPU-accelerated machine learning platform that runs RStudio Server with TensorFlow through reticulate. In this post, I will explain how to… Set up an EC2 instance in AWS with GPU capabilities Install RStudio Server with the latest versions of R Install the correct versions of Python with Recitulate Install TensorFlow and Keras to run deep learning models Set up an EC2 instance Log in to AWS For a new account, select “Root User”.

Every year, the great team at Advent of Code lead by ericwastl develops an “Advent calendar of small programming puzzles for a variety of skill sets and skill levels that can be solved in any programming language you like.” Well, I like R, so in this post I will provide my solutions to the puzzles each day. I plan on using this post to learn and teach and hopefully I am able to stay up-to-date!

As I mentioned in a previous blog post, my family and I have found different activities to do as all our other activities are on hold due to the COVID-19 lockdown. In that post, I looked at first player’s strategy when spinning the big wheel prior to the showcase showdown. In this post, I’ll explore the rarely won game, Pay the Rent Pay The Rent Pay The Rent Pay The Rent is a challenging game.

Its that time of year! Its that time of year again. The time when we dust off the old ESPN fantasy football API R code and fix everything that broke in the last year. Here’s what I hope to show over the next few posts. How to access your ESPN public fantasy football league’s data. How to organize that data and create a few interesting displays. How to create a dashboard to supplement your league’s fun.

Introduction Over Christmas 2017, my wife, Jill, resolved that our family (to include babysitters) would read our children over 1000 new books in 2018. More specifically, 29 December 2017 - 28 December 2018. A goal like this takes dedication, love, and of course, a Data Science-y approach. Special thanks to Ally, Zia, and Bookbuddy for help in accomplishing this goal! Also, special thanks to drob and juliasilge who privde the tidytext package and the entire R open-source community I rely on to do this analysis.

As my experience with statistics, computer science, and the all encompasing term, data science, increases, I have decided that it is time to share some of my projects. Creating this blog serves several purposes. It archives some projects I’ve created. It makes my work public so I can receive (hopefully) constructive feedback to improve my tools/analysis. It helps me share what I’ve learned with the community that has been so helpful to me.