In our next MünsteR R-user group meetup on Monday, June 11th, 2018 Thomas Kluth and Thorben Jensen will give a talk titled Look, something shiny: How to use R Shiny to make Münster traffic data accessible. You can RSVP here: http://meetu.ps/e/F7zDN/w54bW/f
About a year ago, we stumbled upon rich datasets on traffic dynamics of Münster: count data of bikes, cars, and bus passengers of high resolution. Since that day we have been crunching, modeling, and visualizing it.

On April 12th, 2018 I gave a talk about Explaining complex machine learning models with LIME at the Hamburg Data Science Meetup - so if you’re intersted: the slides can be found here: https://www.slideshare.net/ShirinGlander/hh-data-science-meetup-explaining-complex-machine-learning-models-with-lime-94218890
Traditional machine learning workflows focus heavily on model training and optimization; the best model is usually chosen via performance measures like accuracy or error and we tend to assume that a model is good enough for deployment if it passes certain thresholds of these performance criteria.

On April 4th, 2018 I gave a talk about Deep Learning with Keras at the Ruhr.Py Meetup in Essen, Germany. The talk was not specific to Python, though - so if you’re intersted: the slides can be found here: https://www.slideshare.net/ShirinGlander/ruhrpy-introducing-deep-learning-with-keras-and-python
Ruhr.PY - Introducing Deep Learning with Keras and Python von Shirin Glander There is also a video recording of my talk, which you can see here: https://youtu.

In our next MünsteR R-user group meetup on Tuesday, April 17th, 2018 Kai Lichtenberg will talk about deep learning with Keras. You can RSVP here: http://meetu.ps/e/DDY1B/w54bW/f
Although neural networks have been around for quite a while now, deep learning really just took of a few years ago. It pretty much all started when Alex Krizhevsky and Geoffrey Hinton utterly crushed classic image recognition in the 2012 ImageNet Large Scale Visual Recognition Challenge by implementing a deep neural network with CUDA on graphics cards.

I’ll be talking about Deep Learning with Keras in R and Python at the following upcoming meetup:
Ruhr.Py 2018 on Wednesday, April 4th Introducing Deep Learning with Keras and Python Keras is a high-level API written in Python for building and prototyping neural networks. It can be used on top of TensorFlow, Theano or CNTK. In this talk we build, train and visualize a Model using Python and Keras - all interactive with Jupyter Notebooks!

# Join MünsteR for our next meetup on obtaining functional implications of gene expression data with R

In our next MünsteR R-user group meetup on March 5th, 2018 Frank Rühle will talk about bioinformatics and how to analyse genome data.
You can RSVP here: http://meetu.ps/e/DDY1B/w54bW/f
Next-Generation sequencing and array-based technologies provided a plethora of gene expression data in the public genomics databases. But how to get meaningful information and functional implications out of this vast amount of data? Various R-packages have been published by the Bioconductor user community for distinct kinds of analysis strategies.

Last night, the MünsteR R user-group had another great meetup:
Karin Groothuis-Oudshoorn, Assistant Professor at the University of Twente, presented her R package mice about Multivariate Imputation by Chained Equations.
It was a very interesting talk and here are my sketchnotes that I took during it:
MICE talk sketchnotes
Here is the link to the paper referenced in my notes: https://www.jstatsoft.org/article/view/v045i03
“The mice package implements a method to deal with missing data.