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Zeitpunkt              Nutzer    Delta   Tröts        TNR     Titel                     Version  maxTL
Di 06.08.2024 00:00:37    61.975      +4    3.608.594    58,2 Fosstodon                 4.2.10     500
Mo 05.08.2024 00:00:05    61.971      +5    3.606.116    58,2 Fosstodon                 4.2.10     500
So 04.08.2024 00:00:02    61.966      +2    3.604.482    58,2 Fosstodon                 4.2.10     500
Sa 03.08.2024 00:00:05    61.964      -2    3.602.552    58,1 Fosstodon                 4.2.10     500
Fr 02.08.2024 00:00:23    61.966      -4    3.599.813    58,1 Fosstodon                 4.2.10     500
Do 01.08.2024 00:01:11    61.970      +3    3.596.601    58,0 Fosstodon                 4.2.10     500
Mi 31.07.2024 00:00:00    61.967       0    3.593.593    58,0 Fosstodon                 4.2.10     500
Di 30.07.2024 00:00:20    61.967      +2    3.590.362    57,9 Fosstodon                 4.2.10     500
Mo 29.07.2024 00:00:12    61.965      -1    3.587.302    57,9 Fosstodon                 4.2.10     500
So 28.07.2024 00:00:15    61.966       0    3.586.611    57,9 Fosstodon                 4.2.10     500

Di 06.08.2024 14:00

I love the little stories in "Supervised Machine Learning for Science". Of course my favorite is the one on reproducibility.

The book (currently work-in-progress version) is available online: ml-science-book.com/reproducib
Print and ebook expected to come out this fall 🤩

The hurricane forecasting system worked like a charm. Until it didn’t. One hot summer day, the server had a meltdown. And with it vanished the model. Fortunately, the researchers still had the code on an old laptop. All they had to do was retrain and deploy the model. It would take an afternoon, Product Raven estimated. But easier said than done. They had trained the model years ago, and the lead developer had retired, leaving them with a mess. A strange folder structure, mysterious file names, and unclear instructions. In what order should they run the scripts? Why does the training produce different models, sometimes even getting stuck in a local optimum? It was a stressful time for the researchers.

The hurricane forecasting system worked like a charm. Until it didn’t. One hot summer day, the server had a meltdown. And with it vanished the model. Fortunately, the researchers still had the code on an old laptop. All they had to do was retrain and deploy the model. It would take an afternoon, Product Raven estimated. But easier said than done. They had trained the model years ago, and the lead developer had retired, leaving them with a mess. A strange folder structure, mysterious file names, and unclear instructions. In what order should they run the scripts? Why does the training produce different models, sometimes even getting stuck in a local optimum? It was a stressful time for the researchers.

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