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deep learning

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 →

Classifying genres of movies by looking at the poster – A neural approach

Today we will apply the concept of multi-label multi-class classification with neural networks from the last post to
classify movie posters by genre.

Guide to multi-class multi-label classification with neural networks in python

Often in machine learning tasks, you have multiple possible labels for one sample that are not mutually exclusive. This is called a multi-class, multi-label classification problem. Obvious suspects are image classification and text classification, where a document can have multiple… Continue Reading →

Efficient AWS usage for deep learning

When running experiments with deep neural nets you want to use appropriate hardware. Most of the time I work on a Thinkpad laptop with no GPU. This makes experimenting painfully slow. A convenient way is to use an AWS instance,… Continue Reading →

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