By now, some time has passed since NIPS 2016. Consequently, several recaps can be found on blogs. One of them is this one by Eric Jang.
If you want to make your first steps in putting some of the theory presented at NIPS into practice, why not take a look at this slide deck about reinforcement learning in R?
The RStudio Conference also took place, and apparently has been a blast. You can find recaps here and here.
One presentation held at the RStudio conference was by Yihui Xie, about using R Markdown to create blogs and websites using the new
blogdown package. You can find his slides here.
Another presentation at RStudio Conf was given by Hilary Parker of Stitchfix. The last time I wrote this kind of link collection, I linked to a presentation she gave at the New York R Conference. This time around, the topic is similar: Opinionated Analysis Development, which is mindset towards data analysis that could help to scale the work of a growing data science team.
Intro to Bayesian hyper-parameter optimization using Gaussian Process priors in scikit-learn:
Bayesian optimization falls in a class of optimization algorithms called sequential model-based optimization (SMBO) algorithms. These algorithms use previous observations of the loss f, to determine the next (optimal) point to sample f for.
The Data Science Lab at Johns Hopkins University has started a newsletter. Subscribe here.
Yanir Seroussi has built a Bayesian A/B Test calculator which is available here. Read his corresponding blog post for more information.
Justin O’Beirne, who led the development of cartography at Apple, writes extremely detailed essays on Apple and Google Maps, which are beautiful to look at. I always found map apps to be such incredibly useful and magical technology. It’s fun to read opinions from someone who creates them and obsesses about their design. Also, if you’re into data visualization, make sure to check out his cartography reading list (O’Beirne: “One thing you’ll likely notice: almost none of the books are ‘cartography’ books.”).