The goal of lda2vec is to make volumes of text useful to humans (not machines!) while still keeping the model simple to modify. It learns the powerful word representations in word2vec while jointly constructing human-interpretable LDA document representations. We fed our hybrid lda2vec algorithm (docs, code and paper ) every Hacker News comment through 2015. The results reveal what topics and tren
With the commercial successes of machine learning and cloud computing, many business people need just enough math to take advantage of open source frameworks for big data. This video course from Paco Nathan and Allen Day presents useful areas of advanced math in easy-to-digest morsels. If you’re familiar with high school Algebra 2 and basic statistics, you’re good to go. You’ll learn newly introdu
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
Despite two linear algebra classes, my knowledge consisted of “Matrices, determinants, eigen something something”. Why? Well, let’s try this course format: Name the course Linear Algebra but focus on things called matrices and vectors Teach concepts like Row/Column order with mnemonics instead of explaining the reasoning Favor abstract examples (2d vectors! 3d vectors!) and avoid real-world topics
英語版記事を日本語へ機械翻訳したバージョン（Google翻訳）。 万が一翻訳の手がかりとして機械翻訳を用いた場合、翻訳者は必ず翻訳元原文を参照して機械翻訳の誤りを訂正し、正確な翻訳にしなければなりません。これが成されていない場合、記事は削除の方針G-3に基づき、削除される可能性があります。 信頼性が低いまたは低品質な文章を翻訳しないでください。もし可能ならば、文章を他言語版記事に示された文献で正しいかどうかを確認してください。 履歴継承を行うため、要約欄に翻訳元となった記事のページ名・版について記述する必要があります。記述方法については、Wikipedia:翻訳のガイドライン#要約欄への記入を参照ください。 翻訳後、{{翻訳告知|en|Algebra|…}}をノートに追加することもできます。 Wikipedia:翻訳のガイドラインに、より詳細な翻訳の手順・指針についての説明があります。 二
Have you ever asked yourself what monoids and monads are, and particularly why they seem to be so attractive in the field of large-scale data processing? Twitter recently open-sourced Algebird, which provides you with a JVM library to work with such algebraic data structures. Algebird is already being used in Big Data tools such as Scalding and SummingBird, which means you can use Algebird as a me
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