2014年10月27日の「#ssmjp 2014/10」でLTした資料です。詳細はConoHaの技術ブログもご覧ください https://www.conoha.jp/blog/tech/3659.html
![誰得コマンド&オプション35連発](https://cdn-ak-scissors.b.st-hatena.com/image/square/9274ee44ed85afd1e96fe48ab4236769dddce55c/height=288;version=1;width=512/https%3A%2F%2Fcdn.slidesharecdn.com%2Fss_thumbnails%2Fcommandandoption-pptx-120326063903-phpapp01-thumbnail.jpg%3Fwidth%3D640%26height%3D640%26fit%3Dbounds)
2019/10/16 初心者向けCTFのWeb分野の強化法 CTFのweb分野を勉強しているものの本番でなかなか解けないと悩んでいないでしょうか?そんな悩みを持った方を対象に、私の経験からweb分野の強化法を解説します。 How to strengthen the CTF Web field for beginners !! Although you are studying the CTF web field, are you worried that you can't solve it in production? For those who have such problems, I will explain how to strengthen the web field based on my experience. (study group) https://yahoo-osa
The document discusses graph databases and their properties. Graph databases are structured to store graph-based data by using nodes and edges to represent entities and their relationships. They are well-suited for applications with complex relationships between entities that can be modeled as graphs, such as social networks. Key graph database technologies mentioned include Neo4j, OrientDB, and T
「Mobageの大規模データマイニング」PRMU 2011 Big Data and Cloud で講演してきました http://d.hatena.ne.jp/hamadakoichi/20111229/p1Read less
This document summarizes a microservices meetup hosted by @mosa_siru. Key points include: 1. @mosa_siru is an engineer at DeNA and CTO of Gunosy. 2. The meetup covered Gunosy's architecture with over 45 GitHub repositories, 30 stacks, 10 Go APIs, and 10 Python batch processes using AWS services like Kinesis, Lambda, SQS and API Gateway. 3. Challenges discussed were managing 30 microservices, ensur
This document discusses database modeling for a system with companies, departments, and employees. It describes setting up composite primary keys to link the three tables together and using associations and joins to efficiently query across the tables. Methods are defined on the Company model to count employees. A view is created to select large companies with over 1000 employees.Read less
Treasure Dataの基本データフォーマットであるMessagePackと、msgpack-javaでの最適化について紹介します。 Organizations need to perform increasingly complex analysis on data — streaming analytics, ad-hoc querying, and predictive analytics — in order to get better customer insights and actionable business intelligence. Apache Spark has recently emerged as the framework of choice to address many of these challenges. In this session, we
The document discusses the idea of integration by parts, which involves using the product rule in reverse to evaluate integrals that cannot be solved using other methods. It presents the integration by parts formula as u(x)v'(x)dx = u(x)v(x) - u'(x)v(x)dx and works through an example problem of evaluating the integral of xex dx using this formula. The example breaks the integral down into separate
東京大学 松尾研究室が主催する深層強化学習サマースクールの講義で今井が使用した資料の公開版です. 強化学習の基礎的な概念や理論から最新の深層強化学習アルゴリズムまで解説しています.巻末には強化学習を勉強するにあたって有用な他資料への案内も載せました. 主に以下のような強化学習の概念やアルゴリズムの紹介をしています. ・マルコフ決定過程 ・ベルマン方程式 ・モデルフリー強化学習 ・モデルベース強化学習 ・TD学習 ・Q学習 ・SARSA ・適格度トレース ・関数近似 ・方策勾配法 ・方策勾配定理 ・DPG ・DDPG ・TRPO ・PPO ・SAC ・Actor-Critic ・DQN(Deep Q-Network) ・経験再生 ・Double DQN ・Prioritized Experience Replay ・Dueling Network ・Categorical DQN ・Nois
Project Tungsten Bringing Spark Closer to Bare Meta (Hadoop / Spark Conferenc... Hadoop / Spark Conference Japan 2016 キーノート講演資料 『Project Tungsten Bringing Spark Closer to Bare Metal』 Reynold Xin (databricks) ▼イベントページ http://hadoop.apache.jp/hcj2016-program/ http://hcj2016.eventbrite.com/
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