This list is quite small because I hit the history limit on Twitter:( Nevertheless, there are some exciting things - e.g. videos from H2O World 2017.

### Articles:

• https://rviews.rstudio.com/2017/12/11/r-and-tensorflow/ - this article claims that installing keras (for deep learning) is as simple as calling keras::install_keras(). I hope that’s true;)

• https://r-posts.com/leveraging-pipeline-in-spark-trough-scala-and-sparklyr/ - a short comparison of some Spark code written in Scala, and R. Personally, I think that when it comes to working with Spark Scala is a better tool, because you don’t always have nicely structured data ready to put into ML algorithm. Sometimes you need to have more control over underlying data structures, and this cannot be achieved using sparklyr - not everything can be done using dplyr’s style operations.