The document describes various probability distributions that can arise from combining Bernoulli random variables. It shows how a binomial distribution emerges from summing Bernoulli random variables, and how Poisson, normal, chi-squared, exponential, gamma, and inverse gamma distributions can approximate the binomial as the number of Bernoulli trials increases. Code examples in R are provided to simulate sampling from these distributions and compare the simulated distributions to their theoretical probability density functions.
This document introduces deep reinforcement learning and provides some examples of its applications. It begins with backgrounds on the history of deep learning and reinforcement learning. It then explains the concepts of reinforcement learning, deep learning, and deep reinforcement learning. Some example applications are controlling building sway, optimizing smart grids, and autonomous vehicles. The document also discusses using deep reinforcement learning for robot control and how understanding the principles can help in problem setting.
The document describes various probability distributions that can arise from combining Bernoulli random variables. It shows how a binomial distribution emerges from summing Bernoulli random variables, and how Poisson, normal, chi-squared, exponential, gamma, and inverse gamma distributions can approximate the binomial as the number of Bernoulli trials increases. Code examples in R are provided to simulate sampling from these distributions and compare the simulated distributions to their theoretical probability density functions.
This document introduces deep reinforcement learning and provides some examples of its applications. It begins with backgrounds on the history of deep learning and reinforcement learning. It then explains the concepts of reinforcement learning, deep learning, and deep reinforcement learning. Some example applications are controlling building sway, optimizing smart grids, and autonomous vehicles. The document also discusses using deep reinforcement learning for robot control and how understanding the principles can help in problem setting.
Redmine Project Importerプラグインのご紹介
第28回Redmine.tokyoで使用したLTスライドです
https://redmine.tokyo/projects/shinared/wiki/%E7%AC%AC28%E5%9B%9E%E5%8B%89%E5%BC%B7%E4%BC%9A
Redmineのチケットは標準でCSVからインポートできますが、追記情報のインポートは標準ではできないですよね。
チケット情報、追記情報含めてインポートしたいと思ったことはありませんか?(REST-API等用いて工夫されている方もいらっしゃるとおもいますが)
このプラグインは、プロジェクト単位であるRedmineのデータを別のRedmineのDBにインポートします。
例えば、複数のRedmineを一つのRedmineにまとめたいとか、逆に分割したいとかのときに、まるっとプロジェクト単位での引っ越しを実現します。
This is the LT slide used at the 28th Redmine.tokyo event.
You can import Redmine tickets from CSV as standard, but you can't import additional information as standard.
Have you ever wanted to import both ticket information and additional information? (Some people have figured it out using REST-API, etc.)
This plugin imports Redmine data on a project basis into another Redmine database.
For example, if you want to combine multiple Redmines into one Redmine, or split them up, you can move the entire project.
論文紹介:"Visual Genome:Connecting Language and Vision?Using Crowdsourced Dense I...Toru Tamaki
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Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A. Shamma, Michael S. Bernstein, Li Fei-Fei ,"Visual Genome:Connecting Language and Vision?Using Crowdsourced Dense Image Annotations??" IJCV2016
https://link.springer.com/article/10.1007/s11263-016-0981-7
Jingwei Ji, Ranjay Krishna, Li Fei-Fei, Juan Carlos Niebles ,"Action Genome: Actions As Compositions of ?Spatio-Temporal Scene Graphs????" CVPR2020
https://openaccess.thecvf.com/content_CVPR_2020/html/Ji_Action_Genome_Actions_As_Compositions_of_Spatio-Temporal_Scene_Graphs_CVPR_2020_paper.html
論文紹介:PitcherNet: Powering the Moneyball Evolution in Baseball Video AnalyticsToru Tamaki
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Jerrin Bright, Bavesh Balaji, Yuhao Chen, David A Clausi, John S Zelek,"PitcherNet: Powering the Moneyball Evolution in Baseball Video Analytics" CVPR2024W
https://openaccess.thecvf.com/content/CVPR2024W/CVsports/html/Bright_PitcherNet_Powering_the_Moneyball_Evolution_in_Baseball_Video_Analytics_CVPRW_2024_paper.html