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Debugging black-box text classifiers with LIME

Often in text classification, we use so called black-box classifiers. By black-box classifiers I mean a classification system where the internal workings are completly hidden from you. A famous example are deep neural nets, in text classification oftern recurrent or… Continue Reading →

PyData Amsterdam 2018

Last weekend I participated at the PyData Amsterdam 2018 Conference in, you guess it, in Amsterdam. It has been a great conference and I meet a lot of great people and had a very good time in Amsterdam. In this… Continue Reading →

Enhancing LSTMs with character embeddings for Named entity recognition

This is the fifth in my series about named entity recognition with python. The last time we used a CRF-LSTM to model the sequence structure of our sentences. While this approach is straight forward and often yields strong results there are some potential shortcomings. If we haven’t seen a word a prediction time, we have to encode it as unknown and have to infer it’s meaning by it’s surrounding words. To encode the character-level information, we will use character embeddings and a LSTM to encode every word to an vector. We can use basically everything that produces a single vector for a sequence of characters that represent a word.

Guide to word vectors with gensim and keras

  Today, I tell you what word vectors are, how you create them in python and finally how you can use them with neural networks in keras. For a long time, NLP methods use a vectorspace model to represent words…. Continue Reading →

How to build a smart product: Transfer Learning for Dog Breed Identification with keras

This time I will show you how to build a simple “AI” product with transfer learning. We will build a “dog breed identification chat bot”. In this first post, I will show how to build a good model using keras,… Continue Reading →

Detecting Network Attacks with Isolation Forests

In this post, I will show you how to use the isolation forest algorithm to detect attacks to computer networks in python.

A strong baseline to classify toxic comments on Wikipedia with fasttext in keras

This time we’re going to discuss a current machine learning competion on kaggle. In this competition, you’re challenged to build a model that’s capable of detecting different types of toxicity in comments from Wikipedia’s talk page edits. I will show you how to create a strong baseline using python and keras.

Sequence tagging with a LSTM-CRF

This is the fourth post in my series about named entity recognition. The last time we used a recurrent neural network to model the sequence structure of our sentences. Now we use a hybrid approach combining a bidirectional LSTM model and a CRF model. The so called LSTM-CRF is a state-of-the-art approach to named entity recognition.

Guide to sequence tagging with neural networks in python

Named entity recognition series: Introduction To Named Entity Recognition In Python Named Entity Recognition With Conditional Random Fields In Python Guide To Sequence Tagging With Neural Networks In Python Sequence Tagging With A LSTM-CRF Enhancing LSTMs With Character Embeddings For… Continue Reading →

Named entity recognition with conditional random fields in python

This is the second post in my series about named entity recognition. This time, we’re going to look into a more sophisticated algorithm, a so called conditional random field.

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