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

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

Once named entities have been identified in a text, we then want to extract the relations that exist between them. As indicated earlier, we will typically be looking for relations between specified types of named entity. Read more

In the last post, I introduced the U-Net model for segmenting salt depots in seismic images. This time, we will see how to improve the model by data augmentation and especially test time augmentation (TTA). You will learn how to use data augmentation with segmentation masks and what test time augmentation is and how to use it in keras. Read more

Today I’m going to write about a kaggle competition I started working on recently. In the TGS Salt Identification Challenge, you are asked to segment salt deposits beneath the Earth’s surface. So we are given a set of seismic images that are $101 \times 101$ pixels each and each pixel is classified as either salt or sediment. Read more

This is the sixth post in my series about named entity recognition. This time I’m going to show you some cutting edge stuff. We will use a residual LSTM network together with ELMo embeddings, developed at Allen NLP. You will learn how to wrap a tensorflow hub pre-trained model to work with keras. The resulting model with give you state-of-the-art performance on the named entity recognition task.

In this post, I’m going to show you how you can use a neural network from keras with the LIME algorithm implemented in the eli5 TextExplainer class. For this we will write a scikit-learn compatible wrapper for a keras bidirectional LSTM model. The wrapper will also handle the tokenization and the storage of the vocabulary.