Few-shot learning in NLP: many-classes classification from few examples

Posted on Sun 19 August 2018 in posts • Tagged with python, machine learning, deep learning, natural language processing

Few-shot learning on textual data with siamese neural networks

If you're doing machine learning and meet a classification problem with many categories and only a few examples per category, it is usually thought that you're in trouble 😨. Acquiring new data to solve this issue is not always easy or even doable. Luckily, we'll see that efficient techniques exist to deal with this situation 🕺.


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LIME of words: interpreting Recurrent Neural Networks predictions

Posted on Tue 12 September 2017 in posts • Tagged with python, machine learning, deep learning

Interpreting recurrent neural networks with LIME

This is the second part of my blog post on the LIME interpretation model. For a reminder of what LIME is and its purpose, please read the first part. This second part is a quick application of the same algorithm to a deep learning (LSTM) model, while the first part was focused on explaining the predictions of a random forest.


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