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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Alright, this is it: I am officially back! :-D I have not written any blogposts for over a year. Last year, I had the cutest baby boy and ever since then, I did not get around to doing much coding. One of the reasons was that, unfortunately, we did not have the easiest of starts with the little one. Feeding problems led to weight gain problems, so we had to weigh him regularly.

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Today, I am finally getting around to writing this very sad blog post: Before you take my DataCamp course please consider the following information about the sexual harassment scandal surrounding DataCamp! UPDATE from April 26th: Yesterday, DataCamp’s CEO Jonathan Cornelissen issued an apology statement and the DataCamp Board of Directors wrote an update about the situation and next steps (albeit somewhat vague) they are planning to take in order to address the situation.

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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 like loops and conditionals to create your own custom functions. You’ll come away with a toolkit of strategies for analyzing and visualizing data of all sorts using R and RStudio.

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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 around the code had to be minimal. But I’m sure, you can get the gist, even without the book.

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A while ago, I wrote two blogposts about image classification with Keras and about how to use your own models or pretrained models for predictions and using LIME to explain to predictions. Recently, I came across this blogpost on using Keras to extract learned features from models and use those to cluster images. It is written in Python, though - so I adapted the code to R. You find the results below.

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