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ML Interpretability: feature visualization, adversarial example, interp. for language models
In this video, I will be introducing Machine Learning Interpretability, a vast topic that aims at understanding the inner mechanisms of how machine learning models make their predictions, with the aim of debugging them, making them more transparent and trustworthy. I will start by reviewing deep learning and the back-propagation algorithm ...
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