Come join us for the first ever R-themed Brew and View!
We are going to curate a few of our favorite talks into 20 minute clips, and then highlight key points and discuss afterward. Food and a drink provided by IBM!
Adam Ginensky presents: 'Gradient Boosting Machine Learning' by Trevor Hastie at H20.ai World, 2014
Trevor Hastie discusses trees, random forests, and boosting and how to implement them in R. Adam likes this video because it quickly develops the idea for ensemble methods, formalizes it, and shows one how to code it up. For those who want a deeper understanding of one of the top machine learning approaches in practice today, this talk is for you.
Irena Kaplan presents: 'But When You Call Me Bayesian, I Know I’m Not the Only One' by Andrew Gelman at the New York R Conference, 2015.
Andrew Gelman's work on Bayesian statistics and the STAN project is well known. Sit back and watch as Gelman discusses different approaches to Bayesian statistics in the context of political races of past. An excellent talk for those wishing to understand Bayesian Statistics in an interesting and entertaining manner.
Justin Shea presents: 'No-Bullshit Data Science' by Szilard Pafka at R/Finance, 2017.
In his enlightening talks, Szilard has disbanded the myth of the superiority of "Big Data" tools as well "Deep learning" for most Data Science problems. Inviting us to look beyond the hype and focus on the practical application, he illustrates how R has what it takes not only to keep up, but to outperform, with bench-marked examples ranging from loading data to running machine learning models. For those focused on the practical and getting the job done, you'll love this talk.