Introduction to Artificial Neural Networks in R using Keras/TensorFlow

Introduction
This hands-on workshop will be given by Leon Eyrich Jessen. Leon is an assistant professor of bioinformatics in the immunoinformatics and machine learning group, at the section of bioinformatics at DTU Health Tech.
At the workshop, you will be introduced to some of the underlying theory of artificial neural networks (ANNs), how an ANN model is created and we will discuss some of the pitfalls. Finally, you will get to implement, train, tune and apply an ANN based predictor.

Background
In order for a technology to have a societal impact, it needs to be accessible. The Deep Learning hype we are currently experiencing is partially due to the release of open source computational frameworks like TensorFlow. Deep Learning has numerous applications for complex pattern recognition within virtually all branches of industries, e.g. customer churn, self-driving cars, cancer diagnostics, market price forecasting, molecular interactions, etc. The aim of this workshop is to empower you to use the TF technology.
The backbone of the Deep Learning revolution is artificial neural networks (ANNs). Historically, ANNs were available to those with the skills to implement ANN algorithms or to compile existing code. TensorFlow, a general computational framework by google, was only available in Python. However, in 2018 at rstudio::conf in San Diego, RStudio CEO JJ Allaire announced that henceforth Keras and TF will by fully supported in R. JJ Allaire’s presentation included examples of invited blog post from the official RStudio website, one of which was written by Leon.

Level: Beginner

Prerequisites: Workshop participants should bring their own laptop, with the latest versions of R, RStudio and the R-packages ‘tidyverse’ and ‘keras’ installed. Alternatively, a free cloud account is available at https://rstudio.cloud.

Note that the event is a 3 hours code-along feast!