If you didn't have "formal" training in and you are at a point where you can use some models to do "things", but you'd like to understand more about it, my suggestion for best book is: cambridge.org/core/books/machi

If you happen to be into , and (or if you'd like to learn more about the language) you have to check the completely revamped tech.ml stack! github.com/techascent/tech.ml

When evaluating a /#datascience problem always account time for every step:
- framing the problem
- defining data sources
- Data analysis
- Modeling
- Evaluation
- Deployment
If you account only modeling you're saying: "Building this app took 30 minutes!" when that is just the compile/build time

is getting better at :

Here you can find a thorough tutorial on how to use my library (clj-boost) for XGBoost rdisorder.eu/2018/12/03/machin

But now the same functionality - and much more - is achievable with github.com/generateme/fastmath

Please, if you can try these libraries and let us know what you think about them!!! Anything would be really helpful!

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This Mastodon instance is for people interested in technology. Discussions aren't limited to technology, because tech folks shouldn't be limited to technology either!