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
- ETL
- 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!

Mastodon for Tech Folks

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!