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F# and Data Mining

F# and Data Mining. Monday, January 11, 2016. Improve cache performance: matrix multiplication as an example. It is surprising to see mul1() is 10 times slower than mul2(). Mul2: By using j as the inner loop, C[i][j] and B[k][j] have good cache hits, while A[i][k] is a constant during the inner loop. Mul1: In the inner loop, C[i][j] has a constant address, and A[i][k] has good cache hits; however B[k][j] is doomed to cache misses. Saturday, May 16, 2015. Is Rust good for data mining? C has “zero-co...

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F# and Data Mining | fdatamining.blogspot.com Reviews

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F# and Data Mining. Monday, January 11, 2016. Improve cache performance: matrix multiplication as an example. It is surprising to see mul1() is 10 times slower than mul2(). Mul2: By using j as the inner loop, C[i][j] and B[k][j] have good cache hits, while A[i][k] is a constant during the inner loop. Mul1: In the inner loop, C[i][j] has a constant address, and A[i][k] has good cache hits; however B[k][j] is doomed to cache misses. Saturday, May 16, 2015. Is Rust good for data mining? C has “zero-co...

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1 posted by

2 yin zhu

3 27 comments

4 13 comments

5 labels programming language

6 rust

7 setq inf clojure buffer inferior lisp

8 ns example

9 use numeric tower library

10 defn sqr

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F# and Data Mining | fdatamining.blogspot.com Reviews

https://fdatamining.blogspot.com

F# and Data Mining. Monday, January 11, 2016. Improve cache performance: matrix multiplication as an example. It is surprising to see mul1() is 10 times slower than mul2(). Mul2: By using j as the inner loop, C[i][j] and B[k][j] have good cache hits, while A[i][k] is a constant during the inner loop. Mul1: In the inner loop, C[i][j] has a constant address, and A[i][k] has good cache hits; however B[k][j] is doomed to cache misses. Saturday, May 16, 2015. Is Rust good for data mining? C has “zero-co...

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fdatamining.blogspot.com

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F# and Data Mining: October 2014

http://fdatamining.blogspot.com/2014_10_01_archive.html

F# and Data Mining. Saturday, October 4, 2014. A list of references in machine learning and programming languages. Minka, A comparison of numerical optimizers for logistic regression. 160; Estimating a Gamma distribution. 160; Beyond Newton’s method. Steyvers, Multidimensional scaling. Von Luxburg, A tutorial of spectral clustering. Das et. al, Google news personalization: scalable online collaborative filtering. Hofmann, Probabilistic latent semantic analysis. Wickham, Tidy data. 160;&#1...

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F# and Data Mining: October 2012

http://fdatamining.blogspot.com/2012_10_01_archive.html

F# and Data Mining. Monday, October 1, 2012. Setting OCaml/F# Environment in Emacs. For those who don’t know, I did the first 50 Project Euler problem about three years ago (to teach myself F#) and uploaded them at fsharp-euler wikispace. Since then, however, I haven’t had time to do any update. During this weekend, I had the time to play with OCaml on Project Euler problems, and to set up OCaml in Windows. The following shows the Emacs screenshot. Get all val names from ocaml lib mli files*). Funcs = li...

3

F# and Data Mining: November 2012

http://fdatamining.blogspot.com/2012_11_01_archive.html

F# and Data Mining. Tuesday, November 13, 2012. Reflection on Martin Odersky’s Scala Course at Coursera. Some F# news first:. F# and Composite Networks. Got the 4th place. At the Stackoverflow Closed-Question prediction competition. And check out our beautiful visualization, “ mapping the world’s programmers. 8221;, in which the location parser is written in F# and is open sourced. I will write more about this Stackoverflow project in future posts, but today is about Scala…. Code repository link removed.

4

F# and Data Mining: A list of references in machine learning and programming languages

http://fdatamining.blogspot.com/2014/10/a-list-of-references-in-machine.html

F# and Data Mining. Saturday, October 4, 2014. A list of references in machine learning and programming languages. Minka, A comparison of numerical optimizers for logistic regression. 160; Estimating a Gamma distribution. 160; Beyond Newton’s method. Steyvers, Multidimensional scaling. Von Luxburg, A tutorial of spectral clustering. Das et. al, Google news personalization: scalable online collaborative filtering. Hofmann, Probabilistic latent semantic analysis. Wickham, Tidy data. 160;&#1...

5

F# and Data Mining: Is Rust good for data mining?

http://fdatamining.blogspot.com/2015/05/is-rust-good-for-data-mining.html

F# and Data Mining. Saturday, May 16, 2015. Is Rust good for data mining? Rust 1.0 is just released. This is great achievement for Rust team! I have watched Rust for some time. One phase on its website summaries this language rather well: “zero-cost abstraction”. I was attracted to Rust by Poss’s article: Rust for functional programmers. How is it possible for Rust to have the zero-cost abstraction? Col Seq.map (fun e - e*e) Seq.filter (fun e - e%3= 0) Seq.sum. The F# program can ignore these questions&#...

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F# and Data Mining

F# and Data Mining. Monday, January 11, 2016. Improve cache performance: matrix multiplication as an example. It is surprising to see mul1() is 10 times slower than mul2(). Mul2: By using j as the inner loop, C[i][j] and B[k][j] have good cache hits, while A[i][k] is a constant during the inner loop. Mul1: In the inner loop, C[i][j] has a constant address, and A[i][k] has good cache hits; however B[k][j] is doomed to cache misses. Saturday, May 16, 2015. Is Rust good for data mining? C has “zero-co...

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Code Sinppets in F# and Data Mining Blog - Home

Project Hosting for Open Source Software. Code Sinppets in F# and Data Mining Blog. By clicking Delete, all history, comments and attachments for this page will be deleted and cannot be restored. Change History (all pages). Code examples in F# and Data mining blog. http:/ fdatamining.blogspot.com. Last edited May 7, 2011 at 6:03 AM. There is no recommended release for this project. Version 8.4.2015.21029.

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