Blog Archives

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

December 11, 2018
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Code for case study – Customer Churn with Keras/TensorFlow and H2O

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 the content is exclusively for the book, my descriptions...

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

December 4, 2018
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Trust in ML models. Slides from TWiML & AI EMEA Meetup + iX Articles

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...

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Machine Learning Basics – Gradient Boosting & XGBoost

November 28, 2018
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Machine Learning Basics – Gradient Boosting & XGBoost

In a recent video, I covered Random Forests and Neural Nets as part of the codecentric.ai Bootcamp. In the most recent video, I covered Gradient Boosting and XGBoost. You can find the video on YouTube and the slides on slides.com. Both are again in German with code examples in Python. But below, you find the English version of the content, plus code...

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Slides from my talks about Demystifying Big Data and Deep Learning (and how to get started)

November 19, 2018
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On November 7th, Uwe Friedrichsen and I gave our talk from the JAX conference 2018: Deep Learning - a Primer again at the W-JAX in Munich. A few weeks before, I gave a similar talk at two events about Demystifying Big Data and Deep Learning (and how to...

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TWIMLAI European Online Meetup about Trust in Predictions of ML Models

November 12, 2018
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TWIMLAI European Online Meetup about Trust in Predictions of ML Models

At the upcoming This week in machine learning and AI European online Meetup, I’ll be presenting and leading a discussion about the Anchors paper, the next generation of machine learning interpretability tools. Come and join the fun! :-) Date: Tuesda...

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‘How do neural nets learn?’ A step by step explanation using the H2O Deep Learning algorithm.

November 5, 2018
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‘How do neural nets learn?’ A step by step explanation using the H2O Deep Learning algorithm.

In my last blogpost about Random Forests I introduced the codecentric.ai Bootcamp. The next part I published was about Neural Networks and Deep Learning. Every video of our bootcamp will have example code and tasks to promote hands-on learning. While the practical parts of the bootcamp will be using Python, below you will find the English R version of...

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Machine Learning Basics – Random Forest

October 29, 2018
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Machine Learning Basics – Random Forest

A few colleagues of mine and I from codecentric.ai are currently working on developing a free online course about machine learning and deep learning. As part of this course, I am developing a series of videos about machine learning basics - the first v...

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Slides from my talk at the R-Ladies Meetup about Interpretable Deep Learning with R, Keras and LIME

October 16, 2018
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During my stay in London for the m3 conference, I also gave a talk at the R-Ladies London Meetup on Tuesday, October 16th, about one of my favorite topics: Interpretable Deep Learning with R, Keras and LIME. Keras is a high-level open-source deep lear...

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Slides from my m-cubed talk about Explaining complex machine learning models with LIME

October 15, 2018
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Slides from my m-cubed talk about Explaining complex machine learning models with LIME

The last two days, I was in London for the M-cubed conference. Here are the slides from my talk about Explaining complex machine learning models with LIME: Traditional machine learning workflows focus heavily on model training and optimization; the be...

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Sketchnotes from TWiML&AI: Evaluating Model Explainability Methods with Sara Hooker

October 11, 2018
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Sketchnotes from TWiML&AI: Evaluating Model Explainability Methods with Sara Hooker

These are my sketchnotes for Sam Charrington’s podcast This Week in Machine Learning and AI about Evaluating Model Explainability Methods with Sara Hooker: Sketchnotes from TWiMLAI talk: Evaluating Model Explainability Methods with Sara Hooker You...

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