Below you’ll find the complete code and resources used to create the graphs in my talk The Good, the Bad and the Ugly: how to visualize Machine Learning data at this year’s Minds Mastering machines conference. You can find the German slides here: You can find Part 1: The Good, the Bad and the Ugly: how (not) to visualize data here. If you have questions or would like to talk about this article (or something else data-related), you can now book 15-minute timeslots with me (it’s free - one slot available per weekday):

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This is an UPDATE to this old post with updated links & descriptions 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, however. 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 around the code had to be minimal.

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Editor’s note: This is a guest post by Nathaniel Schmucker. He is the founder of The Analyst Code, a blog that provides tools to instill a love of data in individuals of all backgrounds and to empower aspiring analysts. Introduction In this post, we will look at: What is a k-Means analysis? How does the k-Means algorithm work? How do we implement k-Means in R?

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Below you’ll find the complete code used to create the ggplot2 graphs in my talk The Good, the Bad and the Ugly: how (not) to visualize data at this year’s data2day conference. You can find the German slides here: You can also find a German blog article accompanying my talk on codecentric’s blog. If you have questions or would like to talk about this article (or something else data-related), you can now book 15-minute timeslots with me (it’s free - one slot available per weekday):

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Workshop material Because this year’s UseR 2020 couldn’t happen as an in-person event, I have been giving my workshop on Deep Learning with Keras and TensorFlow as an online event on Thursday, 8th of October. You can now find the full recording of the 2-hour session on YouTube and the notebooks with code on Gitlab. If you have questions or would like to talk about this article (or something else data-related), you can now book 15-minute timeslots with me (it’s free - one slot available per weekday):

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I have been working with Keras for a while now, and I’ve also been writing quite a few blogposts about it; the most recent one being an update to image classification using TF 2.0. However, in my blogposts I have always been using Keras sequential models and never shown how to use the Functional API. The reason is that the Functional API is usually applied when building more complex models, like multi-input or multi-output models.

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Recently, I have been getting a few comments on my old article on image classification with Keras, saying that they are getting errors with the code. And I have also gotten a few questions about how to use a Keras model to predict on new images (of different size). Instead of replying to them all individually, I decided to write this updated version using recent Keras and TensorFlow versions (all package versions and system information can be found at the bottom of this article, as usual).

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Dr. Shirin Elsinghorst

Biologist turned Bioinformatician turned Data Scientist

Data Scientist

Münster, Germany