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From Linear Models to Machine Learning (draft) [pdf]
- azuajef 10y agoThanks for sharing. I also recommend "An Introduction to Statistical Learning - with Applications in R": http://www-bcf.usc.edu/~gareth/ISL/ http://www-bcf.usc.edu/~gareth/ISL/
- apathy 10y agoA direct link to the PDF for ISL is here: https://web.stanford.edu/~hastie/local.ftp/Springer/ISLR_print6.pdf https://web.stanford.edu/~hastie/local.ftp/Springer/ISLR_pri... The grownup version of that, ESL, is also available free: https://web.stanford.edu/~hastie/local.ftp/Springer/ESLII_print10.pdf https://web.stanford.edu/~hastie/local.ftp/Springer/ESLII_pr... And for people who are genuinely curious about how this segues into graphical models, NNs, and the autoencoder (maybe the most interesting part of modern NNs), there's https://web.stanford.edu/~hastie/StatLearnSparsity_files/SLS_corrected_1.4.16.pdf https://web.stanford.edu/~hastie/StatLearnSparsity_files/SLS... The more curious or research oriented may appreciate https://web.stanford.edu/~hastie/local.ftp/hastie_glmnet.pdf https://web.stanford.edu/~hastie/local.ftp/hastie_glmnet.pdf I doubt Gareth or Daniela (the primary authors of ISL) would mind my pointing you towards Hastie's archives since both of them were advised by Trevor Hastie during their PhDs. Matloff is a great guy. The chapters on shrinkage and dimension reduction aren't yet written in his book, and since these are important topics, you should consider reading the others. These things are mostly of interest for people who want to draw inference about underlying processes that may be generating observed outcomes. If all you care about is prediction, fit a Random Forest or xgboost GBM or a DNN and be done with it. But if you're actually curious about how complex descriptions of rare events can be thoughtfully analyzed, this is the standard progression. Matloff's book is a great introduction. I particularly like the example on page 204. /ducks
- RhodesianHunter 10y agoHas anyone come across something similar using Python?
- danielmorozoff 10y agoHere you go: https://github.com/JWarmenhoven/ISLR-python https://github.com/JWarmenhoven/ISLR-python R is a popular language for doing any form of stats/ learning theory work for research/ academia. Productionwise not as popular.
- blahi 10y agoIt's pretty popular.
- danielmorozoff 10y agoI haven't seen it, maybe just me. Which companies do you know of?
- blahi 10y agoEbay, Electronic Arts, Google, Microsoft, many financial institutions.
- danielmorozoff 10y agoAre these production systems? I'm a bit blown away that this can work with R. http://adv-r.had.co.nz/Performance.html http://adv-r.had.co.nz/Performance.html
- apathy 10y agoR is a glue language. The fast bits are always written in C++, FORTRAN, or wrapping a BLAS like LAPACK. R-vs-Python is almost never the problem in production. Interpreted-vs-compiled is almost always the issue. (I'm aware of Numba and similar efforts. Last time I tried it, it sucked on nuts. And Theano is a rather specialized bit that most people don't actually need.) JMHO. But I've never seen anyone dealing with truly huge data and inference problems that had the low-level bits in anything other than C++ or FORTRAN. I could imagine that Scala can do a pretty good job now, especially if you use Spark a lot. But R vs Python seems like a really stupid question. Use the one that has the libraries you need.
- FraaJad 10y agoUse of non-monospaced fonts for code fragments in LaTeX composed books must stop.