This article heavily relys on "Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead" by Cynthia Rudin and finally on some of my personal experiences. I will mainly focus on technical issues and leave out most of the governance and ethics related issues that derive from these. Read more

You maybe know the LIME algorithm from some of my earlier blog posts. It can be quite useful to “debug” data sets and understand machine learning models better. But LIME is fooled very easily.

If you are dealing with a large collections of documents, you will often find yourself in the situation where you are looking for some structure and understanding what is contained in the documents. Here I’ll show you a convenient method for discovering and understanding clusters of text documents. Read more

Last weekendPyCon DE and PyData Berlin joined in Berlin for a great conference event that I was lucky to attend. The speaker line-up was great and often it was hard to choose which talk or tutorial to attend. Read more

Deep learning models have shown amazing performance in a lot of fields such as autonomous driving, manufacturing, and medicine, to name a few. However, these are fields in which representing model uncertainty is of crucial importance. Read more

In the previous posts, we saw how to build strong and versatile named entity recognition systems and how to properly evaluate them. But often you want to understand your model beyond the metrics. So in this tutorial I will show you how you can build an explainable and interpretable NER system with keras and the LIME algorithm. Read more

Since a lot of people recently asked me how neural networks learn the embeddings for categorical variables, for example words, I’m going to write about it today. You all might have heard about methods like word2vec for creating dense vector representation of words in an unsupervised way. Read more

You might have heard, that neural language models power a lot of the recent advances in natural language processing. Namely large models like Bert and GPT-2. But there is a fairly old approach to language modeling that is quite successful in a way. Read more

Today we want to build a model, that can identify ingredients in cooking recipes. I use the “German Recipes Dataset”, I recently published on kaggle. We have more than 12000 German recipes and their ingredients list. Read more

This time we have a look into the magnitude library, a feature-packed Python package and vector storage file format for utilizing vector embeddings in machine learning models in a fast, efficient, and simple manner developed by Plasticity. Read more