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Interrogating ML Models in the Wild

Dataiku

In many respects, machine learning (ML) models are not all that different from people. Diving into the process of interrogating model performance to anticipate behavior in the wild, this blog rehashes a recent Dataiku Product Days session.

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Insight: How we do Grace Hopper

Insight

During the first, “Breaking the Black Box: Interpreting Machine Learning Models” by Sheryl Zhang, Kanika Sabharwal, and Rupali Saboo, I learned how to explore VGG16 layers to get insight into features detected during early model training. In this post, I’ll reference the GHC schedule here. This was my second year attending GHC.