Top 10 zero2one.jp Competitors

aiacademy.jp

AI Academy [エーアイアカデミー]はあなたに最適化されたカリキュラムで、Python・機械学習・AIを実践的に学べるプログラミング学習サービスです。Python入門、SQL、機械学習に必要な数学、統計、scikit-learnによる機械学習、Keras+TensorFlowによる深層学習（ディープラーニング）、画像認識、自然言語処理など、人工知能や機械学習に関連するコンテンツが200種類以上のテキストコンテンツから学べます。

e-words.jp

「R」から始まるIT用語の一覧です。

avilen.co.jp

「ChatGPTビジネス研修」は、ChatGPTを業務で活用するために必要なリテラシーを1日で身につけられる研修。ChatGPTの基礎知識、セキュリティのリスク、活用事例を動画とワークで学びます。全社的なChatGPT導入を目指す企業におすすめ。

udemy.com

Udemy is an online learning and teaching marketplace with over 250,000 courses and 73 million students. Learn programming, marketing, data science and more.

qiita.com

これは FOSS4G Advent Calendar 2020 の記事です。 当初は「3D都市モデルのCityGMLをQGISで読み込む？」という記事名で，CityGMLをCityJSONに変換してQGISに読み込む一連の流れについて解説しようとしたのですが，12/8のMI...

scikit-learn.org

Here we try to give some answers to questions that regularly pop up on the mailing list. Table of Contents: About the project- What is the project name (a lot of people get it wrong)?, How do you p...

nuco.co.jp

株式会社NucoはDeep Learningを用いたシステム開発を得意とする企業です。AI, 人工知能を組み込んだシステムの開発は弊社におまかせください。

ibm.com

For more than a century, IBM has been a global technology innovator, leading advances in AI, automation and hybrid cloud solutions that help businesses grow.

arxiv.org

We perform a behavioral experiment of karma, a class of mechanisms for repeated resource allocation with attractive fairness and efficiency properties, in theory. Individuals in these mechanisms bid non-tradable credits that flow from resource consumers to yielders, like karma. Human subjects recruited on Amazon MTurk are repeatedly and randomly paired to bid karma according to time-varying and stochastic individual preferences or urgency to acquire resources. Treatments varied in the dynamic urgency process (frequent moderate urgency versus sporadic high urgency) and the richness of the bidding scheme (binary versus full range). Results are benchmarked against random allocation, and karma achieves a (almost) Pareto improvement over random, despite the MTurk subjects deviating significantly from the theoretically optimal Nash bidding policy. Maximum improvement is attained by subjects that deviate from Nash by up to one karma bid unit on average, and positive improvement is attained with average deviations of up to 3-4 bid units. These findings hold across all treatments, among which no significant differences are found, with the exception of the sporadic high urgency process with binary bidding treatment being (weakly) favorable over others. These results offer behaviorally robust lower bounds for the expected performance of karma in human populations. They also provide guidance for future testing and implementation of karma mechanisms in the real world.