Alan-Tactical Nuclear 🐧 @alan@mastodon.technology

If you didn't have "formal" training in #machinelearning 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: https://www.cambridge.org/core/books/machine-learning/621D3E616DF879E494B094CC93ED36A4

This is simply

#machinelearning #porn

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

When evaluating a #machinelearning/#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

#Clojure is getting better at #machinelearning:

Here you can find a thorough tutorial on how to use my library (clj-boost) for XGBoost https://www.rdisorder.eu/2018/12/03/machine-learning-clojure-xgboost/

But now the same functionality - and much more - is achievable with https://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!