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 completely hidden from you. A famous example are deep neural nets, in text classification often recurrent or convolutional neural nets. But also linear models with a bag of words representation can be considered black-box classifiers, because nobody can fully make sense of thousands of features contributing to a prediction. Read more

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.

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 post I give you an compressed list of the talks I liked most and a pointer to the material of the talk. Creating correct and capable classifiers by IanOzsvald Selection bias: The elephant in the room by Lucas Bernardi Monitoring models in production by Jannes Klaas Unit Testing Data with Marbles by Jane Stewart Adams Elegant data pipelining with Apache Airflow by Bolke de Bruin And of course my own talk “Why Should I Trust You? Read more

This is the fifth post in my series about named entity recognition. If you haven’t seen the last four, have a look now. The last time we used a CRF-LSTM to model the sequence structure of our sentences. We used the LSTM on word level and applied word embeddings. 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. Read more

Word vectors 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. Commonly one-hot encoded vectors are used. This traditional, so called Bag of Words approach is pretty successful for a lot of tasks. Recently, new methods for representing words in a vectorspace have been proposed and yielded big improvements in a lot of different NLP tasks. Read more

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, augmentation, pre-trained models for transfer learning and fine-tuning. In the following posts I will first show you how to build the bot app with telegram and then how to deploy the app on AWS. Read more

In this post, I will show you how to use the isolation forest algorithm to detect attacks to computer networks in python. The term isolation means separating an instance from the rest of the instances. Since anomalies are ‘few and different’ and therefore they are more susceptible to isolation. In a data-induced random tree, partitioning of instances are repeated recursively until all instances are isolated. This random partitioning produces noticeable shorter paths for anomalies since the fewer instances of anomalies result in a smaller number of partitions – shorter paths in a tree structure, and instances with distinguishable attribute-values are more likely to be separated in early partitioning. Read more

This time we’re going to discuss a current machine learning completion on kaggle. In this competition, you’re challenged to build a multi-headed model that’s capable of detecting different types of of toxicity like threats, obscenity, insults, and identity-based hate. You’ll be using a dataset of comments from Wikipedia’s talk page edits. I will show you how to create a strong baseline using python and keras. import pandas as pd import numpy as np import matplotlib. Read more

This is the fourth post in my series about named entity recognition. If you haven’t seen the last three, have a look now. 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. This is a state-of-the-art approach to named entity recognition. Let’s recall the situation from the article about conditional random fields. Read more

This is the third post in my series about named entity recognition. If you haven’t seen the last two, have a look now. The last time we used a conditional random field to model the sequence structure of our sentences. This time we use a LSTM model to do the tagging. At the end of this guide, you will know how to use neural networks to tag sequences of words. For more details on neural nets and LSTM in particular, I suggest to read this excellent post. Read more

This is the second post in my series about named entity recognition. If you haven’t seen the first one, have a look now. Last time we started by memorizing entities for words and then used a simple classification model to improve the results a bit. This model also used context properties and the structure of the word in question. But the results where not overwhelmingly good, so now we’re going to look into a more sophisticated algorithm, a so called conditional random field (CRF). Read more