Publications by Dr. Shirin Glander
My upcoming meetup talks about Deep Learning with Keras and explaining complex Machine Learning Models with LIME
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 bui...
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Join MünsteR for our next meetup on deep learning with Keras and R
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 ...
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Meetup slides: Introducing Deep Learning with Keras
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 wit...
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Sketchnotes from TWiML&AI #124: Systems and Software for Machine Learning at Scale with Jeff Dean
These are my sketchnotes for Sam Charrington’s podcast This Week in Machine Learning and AI about Systems and Software for Machine Learning at Scale with Jeff Dean: Sketchnotes from TWiMLAI talk #124: Systems and Software for Machine Learning at Scale with Jeff Dean You can listen to the podcast here. n this episode I’m joined by Jeff Dean...
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HH Data Science Meetup slides: Explaining complex machine learning models with LIME
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 work...
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Look, something shiny: How to use R Shiny to make Münster traffic data accessible. Join MünsteR for our next meetup!
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 ...
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Sketchnotes from TWiML&AI #121: Reproducibility and the Philosophy of Data with Clare Gollnick
These are my sketchnotes for Sam Charrington’s podcast This Week in Machine Learning and AI about Reproducibility and the Philosophy of Data with Clare Gollnick: Sketchnotes from TWiMLAI talk #121: Reproducibility and the Philosophy of Data with Clare Gollnick You can listen to the podcast here. In this episode, i’m joined by Clare Gollnic...
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Update: Can we predict flu outcome with Machine Learning in R?
Since I migrated my blog from Github Pages to blogdown and Netlify, I wanted to start migrating (most of) my old posts too – and use that opportunity to update them and make sure the code still works. Here I am updating my very first machine learning post from 27 Nov 2016: Can we predict flu deaths with Machine Learning and R?. Changes are mark...
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Slides from my JAX 2018 talk: Deep Learning – a Primer
Here I am sharing the slides for a talk that my colleague Uwe Friedrichsen and I gave about Deep Learning – a Primer at the JAX conference on Tuesday, April 24th 2018 in Mainz, Germany. Slides can be found here: https://www.slideshare.net/ShirinGlander/deep-learning-a-primer-95197733 Deep Learning is one of the “hot” topics in the AI area ...
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Comparing dependencies of popular machine learning packages with `pkgnet`
When looking through the CRAN list of packages, I stumbled upon this little gem: pkgnet is an R library designed for the analysis of R libraries! The goal of the package is to build a graph representation of a package and its dependencies. And I thought it would be fun to play around with it. The little analysis I ended up doing was to compare ...
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