Writing a textbook revealed: AI-for-science hinges on "details", not architectures
bravo_abad · x · 2026-09-21
While writing a Spanish-language AI textbook for science students, the author realized that decisions filed under "technical details" — how you split data, what labels mean, which metric you report, where thresholds go — matter more than architectures in scientific settings. Each is a claim about what you're asserting, often made silently by library defaults.
Key points:
- Start from the data you have: labs with 50 patient samples or 100 compounds are far from deep learning's famous big-data regime; regularized regression, random forests, SVMs, and Gaussian processes are often better, especially when uncertainty and honest validation matter.
- The full piece organizes 11 such ideas by the questions they answer, each with a figure and a transferable lesson.
Related event: Eleven ML Rules for Scientists: Details Matter More Than Model Architecture(4 posts)→
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