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. We use the eli5 TextExplainer which is based on LIME and the 20newsgroup data set to show how LIME can fail.
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. The standard deep learning… Continue Reading →
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.
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. In this article you will learn what an embedding layer really is and how neural nets can learn representations for categorical variables with it.
Today, I want to show you how you can build an NLP application without explicitly labeled data. I use the “German Recipes Dataset” I recently published on kaggle, to build a neural network model, that can identify ingredients in cooking… Continue Reading →
This time we have a look into the magnitude library, a feature-packed Python package for utilizing vector embeddings in machine learning models in a fast, efficient, and simple manner. We want to utilize the embeddings magnitude provides and use them in keras.
This is the second post of my series about understanding text datasets. Here we use the named entities to get some information about our data set.
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