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Exciting news!
Our book Supervised Machine Learning for Science is now published! 🥳🥳🥳
Available in hardcover, paperback, eBook, and PDF. You can even read it online for free!
Christoph Molnar 🦋 christophmolnar.bsky.social
5,063 posts
Christoph Molnar 🦋 christophmolnar.bsky.social
@ChristophMolnar
- BREAKING: IBM makes game-changing move, invests $42 in ChatGPT Pro to revolutionize IBM Watson capabilities
- I have the solution for detecting AI-generated text.
- 2 years, 250 pages, 1,219 commits, and 78,480 words: I am very proud to say that today I published the 1st edition of "Interpretable Machine Learning". 🎉🎉🎉 Web: christophm.github.io/interpretable-… Leanpub: leanpub.com/interpretable-…
- When your machine learning model is confronted with data it has not been trained on.
00:00 - You can't "train" a model. The model always exists. It existed before you were born and it exists after your death. You can only find the model. "Training" is just your way of looking for the model's location in the infinite hypothesis space and binding its essence to silicon
- I write my books with vim. Why tough? While there are many good reasons to use Vim for book writing, one stands out: I don't know how to exit Vim.
- Machine Learning concepts as animal GIFs A thread 🧵
- Watching the k-means cluster slowly converge
00:00 - Ready to nerd out on random forests? Someone wrote an entire Ph.D. thesis on random forests ❤️ 200 pages of gems. Highlights: Why ensembles work, interpretability of forests, and making random forests work on huge datasets. buff.ly/3QbTC9o
- A lot of machine learning research has detached itself from solving real problems, and created their own "benchmark-islands". How does this happen? And why are researchers not escaping this pattern? A thread 🧵
- Evaluating a model on training data is like asking your mom if you look good.
- The mean is the best prediction model • Needs no features • Easy to compute • No overfitting • Interpretable • Optimizes L2 • Analytical solution
