Publications by Dr. Shirin Glander

Trust in ML models. Slides from TWiML & AI EMEA Meetup + iX Articles

04.12.2018

Here you find my slides from last nights TWiML & AI EMEA Meetup about Trust in ML models, where I presented the Anchors paper by Carlos Guestrin et al.. I have also just written two articles for the German IT magazin iX about the same topic of Explaining Black-Box Machine Learning Models: A short article in the iX 12/2018 The full article in iX...

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Code for case study – Customer Churn with Keras/TensorFlow and H2O

11.12.2018

This is code that accompanies a book chapter on customer churn that I have written for the German dpunkt Verlag. The book is in German and will probably appear in February: https://www.dpunkt.de/buecher/13208/9783864906107-data-science.html. The code you find below can be used to recreate all figures and analyses from this book chapter. Because t...

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February 21st & 22nd: End-2-End from a Keras/TensorFlow model to production

06.01.2019

Registration is now open for my 1.5-day workshop on how to develop end-2-end from a Keras/TensorFlow model to production. It will take place on February 21st & 22nd in Berlin, Germany. Please register by sending an email to [email protected] with the following information: name company/institute/affiliation address for invoice phone number refe...

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Don’t reinvent the wheel: making use of shiny extension packages. Join MünsteR for our next meetup!

07.01.2019

In our next MünsteR R-user group meetup on Tuesday, February 5th, 2019, titled Don’t reinvent the wheel: making use of shiny extension packages., Suthira Owlarn will introduce the shiny package and show how she used it to build an interactive web app for her sequencing datasets. You can RSVP here: http://meetu.ps/e/Gg5th/w54bW/f Shiny is a po...

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How do Convolutional Neural Nets (CNNs) learn? + Keras example

08.01.2019

As with the other videos from our codecentric.ai Bootcamp (Random Forests, Neural Nets & Gradient Boosting), I am again sharing an English version of the script (plus R code) for this most recent addition on How Convolutional Neural Nets work. In this lesson, I am going to explain how computers learn to see; meaning, how do they learn to recogni...

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Lecture slides: Real-World Data Science (Fraud Detection, Customer Churn & Predictive Maintenance)

15.01.2019

These are slides from a lecture I gave at the School of Applied Sciences in Münster. In this lecture, I talked about Real-World Data Science and showed examples on Fraud Detection, Customer Churn & Predictive Maintenance. Real-World Data Science (Fraud Detection, Customer Churn & Predictive Maintenance) von Shirin Glander The slides were cre...

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My course on Hyperparameter Tuning in R is now on Data Camp!

16.01.2019

I am very happy to announce that (after many months) my interactive course on Hyperparameter Tuning in R has now been officially launched on Data Camp! Course Description For many machine learning problems, simply running a model out-of-the-box and getting a prediction is not enough; you want the best model with the most accurate prediction. On...

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How to prepare data for NLP (text classification) with Keras and TensorFlow

22.01.2019

In the past, I have written and taught quite a bit about image classification with Keras (e.g. here). Text classification isn’t too different in terms of using the Keras principles to train a sequential or function model. You can even use Convolutional Neural Nets (CNNs) for text classification. What is very different, however, is how to prepa...

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Book review: Beyond Spreadsheets with R

30.01.2019

Disclaimer: Manning publications gave me the ebook version of Beyond Spreadsheets with R – A beginner’s guide to R and RStudio by Dr. Jonathan Carroll free of charge. Beyond Spreadsheets with R shows you how to take raw data and transform it for use in computations, tables, graphs, and more. You’ll build on simple programming techniques l...

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Launching codecentric.AI Bootcamp course!

07.02.2019

Today, I am happy to announce the launch of our codecentric.AI Bootcamp! This bootcamp is a free online course for everyone who wants to learn hands-on machine learning and AI techniques, from basic algorithms to deep learning, computer vision and NLP. However, the course language is German only, but for every chapter I did, you will find an Engl...

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