functor.tokyo -- How to get into Machine Learning for a Haskeller
I've been interested in Haskell for about 5 years now. I've been using it professionally for about 3 years. Most of the work I've done so far has been web-related. A large part has been writing web application backends in Haskell. I really enjoy using Haskell as a programming language, but I would like to expand my skill set. When looking to level up, it seems like a lot of Haskellers focus on imp
GitHub - mikeizbicki/HLearn: Homomorphic machine learning
HLearn is a high performance machine learning library written in Haskell. For example, it currently has the fastest nearest neighbor implementation for arbitrary metric spaces (see this blog post). HLearn is also a research project. The research goal is to discover the "best possible" interface for machine learning. This involves two competing demands: The library should be as fast as low-level li
Haskellの機械学習ライブラリHLearn - Qiita
import HLearn.Models.Distributions import HLearn.Algebra.Models.HomTrainer dataset = [1,2,3,4,5,6] dist = train dataset :: Normal Integer Integer -- 引用元： http://izbicki.me/blog/functors-and-monads-for-analyzing-data -- いろいろな色のビー玉の定義 data Marble = Red | Pink | Green | Blue | White deriving (Read,Show,Eq,Ord) bagOfMarbles = [ Pink,Green,Red,Blue,Green,Red,Green,Pink,Blue,White ] -- カテゴリーデータ（このカテゴリーと
PRML３章の線形回帰を実装した - mmitouの日記
PRML３章の線形回帰を実装した。ベイズ線形回帰は未実装。 中谷さんのgihyo.jp連載記事「機械学習 はじめよう」 第11回 "線形回帰を実装してみよう"が非常に参考になりました。 http://gihyo.jp/dev/serial/01/machine-learning/0011?page=1 実装は以下の通り。 import System.Random import Numeric.LinearAlgebra import Graphics.Gnuplot.Simple import Control.Applicative -- ノイズを付与する addNoise :: (RandomGen g, Random a, Num a) => (a, a) -> [a] -> g -> [a] addNoise range xs g = zipWith (+) xs (randomRs
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Backpropogation is Just Steepest Descent with Automatic Differentiation
Haskell Data Analysis Cookbook
