Aimée Gott, Education Practice Lead

I always find it difficult to pick highlights from a conference and the eRum 2018 team did a fantastic job of making it difficult for me once again, so here goes…

Day One

The first day offered a huge choice in workshops, but teaching one of them meant we didn’t make it to any of the others. However, everyone we spoke to had great things to say about them all. In fact, we were overwhelmed by the turnout for our own workshop on the keras package (and we have to give a shout out to Mark Sellors for setting up and monitoring the server for us). By the way, if you missed out, you can sign up for the workshop at EARL London in September.

Day Two

Tuesday might have been a rainy start outside but inside we were mesmerised by the transparent roof in the Akvarium Klub. For team Mango, the morning mostly involved cat restocking, so we were really grateful for the live streaming that enabled us to keep up with everything going on in the main room.

My favourite presentations included:

Having newly been introduced to the recipes package I particularly enjoyed seeing Edwin Thoen talk about how to add your own data preparation steps and checks.

Olga Mierzwa-Sulima presented six packages to add functionality to shiny apps. These cover UI aspects like using semantic elements in shiny or easily exchanging themes for the app as well as user management aspects like authentication and controlling the level of access for different users. She also covered additional functionality like making routing possible with shiny and building multi-language apps.

Jeroen Ooms spoke about using Rust code in R packages. Rust is a new system programming language and can be an alternative to C/C++. Jeroen mentioned several advantages including Rust being memory safe and as fast as C/C++ while being far safer. It ships with a native package manager (cargo) and does not need a runtime library which means that the binary (R) package does not depend on Rust or cargo. He stressed that it’s easy to wrap Rust libraries into R packages so hopefully soon the selection of tools available from R will be even more varied.

A particular highlight for Doug Ashton came on Tuesday afternoon with three complementary talks on machine learning. With all the buzz to deal with in the ML world right now, Doug thought the practical talks from three level-headed practioners were very useful:

First Erin LeDell, Chief Machine Learning Scientist at h2o, gave an excellent talk on their automl package – a system they’ve been working hard on to run several different algorithms and select the best. Doug’s favourite part was their automated model ensembling (aka model stacking) that provides the best mix of all the algorithms.

Szilárd Pafka, Chief Scientist at Epoch followed on with a presentation on the provactively titled “Better than deep learning: Gradient Boosted Machines in R”, where he talked about why the majority of ML problems he sees are not best suited for deep learning. Szilárd also gave a nice overview of the best performing algorithms for gbm. (Doug’s note to self was to check out Microsoft’s lightgbm and xgboost with gpu.)

I'll get a lot of ??? for this, but here it is a table comparison of best gradient boosting machine (GBM) implementations #xgboost #lightgbm and @h2oai first presented today at #eRum2018 R conference in Budapest. Based on https://t.co/sDKtiiSbBo pic.twitter.com/BbqH4UKyr4

— Szilard (@DataScienceLA) May 15, 2018

Going back to the theme of automation Andrie de Vries, Solutions Engineer at RStudio, took us through how to tune your tensorflow models using the tfruns package to run grid/random search over the hyperparameter space. This is very timely as we are often asked about how to select the right network topology and until now we’ve largely hand-tuned. Andrie then took us through an example where deep learning certainly is the right choice—image/pattern recognition with convolutional neural nets (CNNs)—and taking our example from the keras workshop significantly improved the accuracy with automated tuning – 👏.

After the rainy to start the day we were all relieved to see it had cleared up by the time the evening event (a river cruise) came around – we were lucky to have stunning views of Budapest from the Danube; the sunset on the Parliament building with stormy skies overhead was incredible.

Day Three

On Wednesday morning Roger Bivand did a great job of talking us all through some of the very important history of R – you all knew “_” was once an assignment operator, right?

RStudio’s Barbara Borges Ribeiro showed off a cool shiny app for drilling down into data and making use of the dynamic insertion of UI. Unfortnately I missed her talk, but managed to get a demo of later in the day – you can take a look at the app on GitHub.

The biggest highlight for me though were the people. Without a doubt it was one of the friendliest conferences I have attended. Everyone was happy to share their experiences, answer your questions and point you in the direction of tools to look at later. Importantly, everyone was made to feel welcome, from the most-experienced to the newest R users.

The videos from all talks are being made available, check the conference homepage for the link.

Congratulations to the whole organising committee, led by Gergely Daroczi, for putting on such a great event. Mango are certainly looking forward to the next eRum conference!