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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Registration is now open for my 1.5-day workshop on deep learning with Keras and TensorFlow using R. It will take place on November 8th & 9th in Munich, Germany. You can read about one participant’s experience in my workshop: Big Data – a buzz word you can find everywhere these days, from nerdy blogs to scientific research papers and even in the news. But how does Big Data Analysis work, exactly?

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In our next MünsteR R-user group meetup on Tuesday, August 28th, 2018 Jenny Saatkamp will give a talk titled Blog Mining: Deriving the success of blog posts from metadata and text data. You can RSVP here: http://meetu.ps/e/F7zDN/w54bW/f In our next MünsteR Meetup, Jenny Saatkamp will present her Blog Mining analysis, which is based on 1.500 blog posts from the codecentric company blog (https://blog.codecentric.de/) and makes use of different mining techniques for metadata and text data.

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This is code that will encompany an article that will appear in a special edition of a German IT magazine. The article is about explaining black-box machine learning models. In that article I’m showcasing three practical examples: Explaining supervised classification models built on tabular data using caret and the iml package Explaining image classification models with keras and lime Explaining text classification models with xgboost and lime

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This is code that will encompany an article that will appear in a special edition of a German IT magazine. The article is about explaining black-box machine learning models. In that article I’m showcasing three practical examples: Explaining supervised classification models built on tabular data using caret and the iml package Explaining image classification models with keras and lime Explaining text classification models with xgboost and lime

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After posting my short blog post about Text-to-speech with R, I got two very useful tips. One was to use the googleLanguageR package, which uses the Google Cloud Text-to-Speech API. And indeed, it was very easy to use and the resulting audio sounded much better than what I tried before! Here’s a short example of how to use the package for TTS: Set up Google Cloud and authentification You first need to set up a Google Cloud Account and provide credit card information (the first year is free to use, though).

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These are the slides from my workshop: Introduction to Machine Learning with R which I gave at the University of Heidelberg, Germany on June 28th 2018. The entire code accompanying the workshop can be found below the video. The workshop covered the basics of machine learning. With an example dataset I went through a standard machine learning workflow in R with the packages caret and h2o: reading in data exploratory data analysis missingness feature engineering training and test split model training with Random Forests, Gradient Boosting, Neural Nets, etc.

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Dr. Shirin Glander

Biologist turned Bioinformatician turned Data Scientist

Data Scientist

Münster, Germany