I’ve recently added new bloggers to the site, leading to an influx of posts (142 new posts on the site, just last week). And while I love R, I do not have the time to read them all. Luckily, these posts are also published on R-bloggers twitter page, where over 62K followers see the new posts and decide if to give them a “like” or not. So I thought it might be helpful to sort all of last weeks posts (that got at least 15 likes), just to make sure I will not miss out on reading a particular popular post. I’m posting it here in the hopes that this would be helpful to others as well. Here is the list:

By George Seif, AI / Machine Learning Engineer

Editor’s note: This piece was written in collaboration with the MAA Center

Manfred te Grotenhuis passed away. He was a respected sociologist, statistician, and teacher. I’ll leave it to others to comment on these achievements. To me, he was my teacher and mentor in statistics, and a dear colleague. Textbooks and other teachers have a lot to say about the theory of statistics, but it was Manfred who taught me the joy and intuition of doing statistics.

StochasticNet Deep neural networks is a branch in machine learning that has seen a meteoric rise in popularity due to its powerful abilities to represent and model high-level abstractions in highly complex data. One area in deep neural networks that is ripe for exploration is neural connectivity formation. A pivotal study on the brain tissue of rats found that synaptic formation for specific functional connectivity in neocortical neural microcircuits can be surprisingly well modeled and predicted as a random formation. Motivated by this intriguing finding, we introduce the concept of StochasticNet, where deep neural networks are formed via stochastic connectivity between neurons. Such stochastic synaptic formations in a deep neural network architecture can potentially allow for efficient utilization of neurons for performing specific tasks. To evaluate the feasibility of such a deep neural network architecture, we train a StochasticNet using three image datasets. Experimental results show that a StochasticNet can be formed that provides comparable accuracy and reduced overfitting when compared to conventional deep neural networks with more than two times the number of neural connections. …

Today I received in the mail a copy of the short book published by edp sciences after the courses we gave last year at the astrophysics summer school, in Autrans. Which contains a quick introduction to ABC extracted from my notes (which I still hope to turn into a book!). As well as a longer coverage of Bayesian foundations and computations by David Stenning and David van Dyk.

How to Use IoT Datasets in #AI Applications

Having all the R functions, all libraries and any kind of definitions (URL, links, working directories, environments, memory, etc) in one file is nothing new, but sometimes a lifesaver.

There are so many beautiful “π” arts everywhere, and I wanted to practice ggplot2 by mimicing those arts further more. Another pi art caught my eye is random walk of pi digits. Here’s one of examples in WIRED magazine.