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.
One of the latest milestones in pre-training and fine-tuning in natural language processing is the release of BERT. This is a new post in my NER series. I will show you how you can fine-tune the Bert model to do state-of-the art named entity recognition in pytorch.
This is the first post of my series about understanding text data sets. In practice, you often want and need to know, what is going on in your data. In this post we will focus on applying a Latent Dirichlet allocation (LDA) topic model to the “Quora Insincere Questions Classification” data set on kaggle.
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. I covered named entity… Continue Reading →
An important part of every machine learning project is the proper evaluation of the performance of the system. In this post I will show you how evaluate sequence models with token-based labels. This way you can get a proper understanding of you sequence model performance.
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… Continue Reading →