Haskell Data Analysis Cookbook
Hint: Click On image to View more available high quality Photos. GET Haskell Data Analysis CookbookExplore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes Overview A practical and concise guide to using Haskell when getting to grips with data analysis Recipes for every stage of data analysis, from collection to visualization In-depth exampl
Functors and monads for analyzing data
Functors and monads are powerful design patterns used in Haskell. They give us two cool tricks for analyzing data. First, we can “preprocess” data after we’ve already trained a model. The model will be automatically updated to reflect the changes. Second, this whole process happens asymptotically faster than the standard method of preprocessing. In some cases, you can do it in constant time no
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
1
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
処理を実行中です
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
Hal Daumé III - software
