If you’re interested in diving into Machine Learning and playing with Pluto notebooks for various demos, check out a new Springer book:
Machine Learning with Julia: An Algorithmic Exploration by Jeremiah D. Deng
The demo notebooks can be downloaded from JuliaHub, search for public notebooks “MLwithJulia-demos”
I had a look on the index, very cool, thank you!
Just a few notes:
- you wrote in the preface that Reinforcement Learning is included, but I don’t see it in the book
- how do you deal/organize stuff that are themselves a mix of stuff ? For example, Autoencoder as dimensionality reductions, neural networks in the context of reinforcement learning, classifiers/regressors for missingness imputation.. I think the cool part of ML is indeed when you start mixing and matching the various methods…
- for the feature importance you have also Sobol indexes, I use them in the BetaML FeatureRanker, and they seems to provide good rankings
Let me know if you are open to share in private PDFs for genuine reviews 