It's Drew Conway. The guy is good, and he makes complex things very accessible. Sometimes, you need to understand the matrices behind the process, and that's great, lots and lots of books out there about the math. But sometimes you need to group up some data and you need a quick way to get it done mostly well. There aren't as many books out there to do this as there should be, and I look forward to this one.
I like (& use) R, but to say it is "for hackers" seems strange to me -- not really a "hacker" language. Seems like a bit of a marketing strategy(/gimmick) to toss that word into the title. Looks like a promising book, though i'd much prefer it be in a scripting language like Python.
I haven't used R, but I've heard good things about it. Why isn't it for "hackers"?
If anything, it seems like a language more suited for hackers than Python for tasks involving math--it looks like it has a more integrated environment for fast iteration , particularly for generating nice plots of the data you're working with. It also seems like a language designed and optimized exactly for this sort of activity.
This looks like it's using R to teach, primarily. I'm partial to "Collective Intelligence" (Toby Segaran's book)-- it's written with all python examples and appears to have much of the same content and approach (practical application > theoretical underpinnings).
I just received my copy of "Collective Intelligence" in the mail this morning, and immediately sat down and starting reading. I've been reading for the past several hours, non-stop. It's that good. Perhaps the best part is the chapter at the end of the book that neatly and concisely sums up all the major ML algorithms: neural networks, SVM, kNN, k-means, decision trees, Bayesian classifiers, etc. etc. This chapter alone is worth the price of the book. I only wish it were longer (and it's not a short book).
If you're using Python, what are you using for your stats libraries?
R is, generally, the go to language for stats work I've found. It's certainly used a lot in the financial world for statistical modelling, and all the libraries in it are well tested which is a big plus in my book.
I liked "Programming Collective Intelligence," as it fairly clearly introduces how the algorithms operate by implementing them from scratch.
I found the coding style a bit non-pythonic, though (two space indentation?), and in practice you'd be better served using one of the many ML libraries (e.g., scikit-learn [1]), which aren't introduced.
[+] [-] mwexler|14 years ago|reply
[+] [-] plessthanpt05|14 years ago|reply
[+] [-] tikhonj|14 years ago|reply
If anything, it seems like a language more suited for hackers than Python for tasks involving math--it looks like it has a more integrated environment for fast iteration , particularly for generating nice plots of the data you're working with. It also seems like a language designed and optimized exactly for this sort of activity.
[+] [-] rd108|14 years ago|reply
[+] [-] eric_bullington|14 years ago|reply
[+] [-] fuzzythinker|14 years ago|reply
[+] [-] elemeno|14 years ago|reply
R is, generally, the go to language for stats work I've found. It's certainly used a lot in the financial world for statistical modelling, and all the libraries in it are well tested which is a big plus in my book.
[+] [-] gammarator|14 years ago|reply
I found the coding style a bit non-pythonic, though (two space indentation?), and in practice you'd be better served using one of the many ML libraries (e.g., scikit-learn [1]), which aren't introduced.
[1] http://scikit-learn.org/stable/index.html
[+] [-] showerst|14 years ago|reply
http://www.scribd.com/doc/80949907/TOC-ML4Hackers
Exciting!