Data Science #35

In this issue: alternatives to cosine similarity; understanding Gaussians; scaling to multi-terabyte datasets; coding for structured generation with LLMs; a tutorial on bayesian optimization; methods for comparing spatial patterns in raster data; and more.

The sponsor of this issue is GitNotebooks .

• Alternatives to cosine similarity
• Understanding Gaussians
• Lessons learned from scaling to multi-terabyte datasets
• [Sponsored] Jupyter Notebook Review Tool for GitHub

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• What is confounding ?
• [Tutorial] Coding for structured generation with LLMs
• A tutorial on bayesian optimization
• [R] Nested unit tests with testthat
• Methods for comparing spatial patterns in raster data

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