Mindful Modeler
Christoph Molnar, Vasilis GkolemisBetter machine learning by thinking like a statistician. About model interpretation, paying attention to data, and always staying critical.
| Platform |  | Pricing | Only free issues | Publishes | Weekly |
|---|---|---|---|---|---|
| Issues | 127 | Subscribers | Read | mindfulmodeler.substack.com |
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Latest Issues
Recent posts by this newsletter. Browse the email archive.
Old-School Interpretability for LLMs
Mechanistic interpretability has taken most of the spotlight when it comes to the interpretation of LLMs. Unfortunately, except for some initial sparks, mechanistic interpretability hasn’t delivered. Especially not for users of large langua...
Building Block View of ML
Inspired by the modular nature of neural networks, can we see all machine learning algorithms through the lens of building blocks?
I quit writing Building Blocks of ML
8:00 AM. A cup of steaming coffee. Time to write.
Making classifier evaluation less confusing
ROC AUC, accuracy, specificity, log loss, calibration, confusion matrix, Brier score — evaluation of ML classifiers has caused me lots of confusion (pun intended). This post gives an overview of the big groups of evaluation metrics.
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Authors
The writers behind this newsletter.
Writer and machine learning expert
Machine Learning researcher; special focus on Explainability and Uncertainty Quantification. Enthusiast of open-source projects.
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