Genetic Algorithms for Multivariate Statistical Models from Large-Scale Functional Genomics Data (galgo)Build multivariate predictive models from large datasets having far larger number of features than samples such as in functional genomics datasets. Tre …

In this blog post, I’ll explain the new functionality of the OpenImageR package, SLIC and SLICO superpixels (Simple Linear Iterative Clustering) and their applicability based on an IJSR article. The author of the article uses superpixel (SLIC) and Clustering (Affinity Propagation) to perform image segmentation. The article was reproduced (and extended with Kmeans) using the latest versions of the OpenImageR and ClusterR packages.

T-Mobile, the global telecommunication company, is using R in production to automatically classify text messages to customer service  and route them to an agent that can help. The AI@T-mobile team used the keras library in R to build a natural language processing engine with Tensorflow, and deployed it to production as a docker container. The MRAN Time Machine ensures the container gets fixed R package versions for reproducibility. 

If you’re an R/RStudio user who has migrated to Mojave (macOS 10.14) or are contemplating migrating to it, you will likely eventually run into an issue where you’re trying to access resources that are in Apple’s new hardened filesystem sandboxes.

Benjamin Carlisle writes:

By Genevieve Hayes, Stitchdata.

The 4.1.0 release of simmer, the Discrete-Event Simulator for R, is on CRAN. As per request in the mailing list, now get_global() is able to work inside a generator function. Moreover, the new add_global() method attaches a global attribute to a simulator.

Top 5 trends in Data Science

Motivation There are dozens of machine learning algorithms out there. It is impossible to learn all their mechanics, however, many algorithms sprout from the most established algorithms, e.g. ordinary least squares, gradient boosting, support vector machines, tree-based algorithms and neural networks. At STATWORX we discuss algorithms daily to evaluate their usefulness for a specific project. In any case, understanding these core algorithms is key to most machine learning algorithms in the literature.

Probabilistic Programming versus Machine Learning