Drew Conway

John Myles White

萩原 正人

奥野 陽

水野 貴明

木下 哲也

本書はプログラミングの素養がある読者向けに、数学的・理論的な知識が必要なくても読めるよう、理論より実践に重きを置いて書かれた機械学習の入門書です。難しい理論的な解説はできるだけ避け、実際のテクニックを詳述する、プログラマの視点に立ったプログラマ向けの内容です。大規模データの処理に威力を発揮する機械学習の実践的な知識とテクニックを習得したいと考えるプログラマに最適の一冊です。

内容詳細

入門 機械学習

When looking for ways to improve your website, how do you decide which changes to make? And which changes to keep? This concise book shows you how to use Multiarmed Bandit algorithms to measure the real-world value of any modifications you make to your site. Author John Myles White shows you how this powerful class of algorithms can help you boost website traffic, convert visitors to customers, and increase many other measures of success. This is the first developer-focused book on bandit algorithms, which were previously described only in research papers. You’ll quickly learn the benefits of several simple algorithms—including the epsilon-Greedy, Softmax, and Upper Confidence Bound (UCB) algorithms—by working through code examples written in Python, which you can easily adapt for deployment on your own website. Learn the basics of A/B testing—and recognize when it’s better to use bandit algorithms Develop a unit testing framework for debugging bandit algorithms Get additional code examples written in Julia, Ruby, and JavaScript with supplemental online materials

著者：John Myles Whiteの検索結果

入門機械学習

Bandit Algorithms for Website Optimization: Developing, Deploying, and Debugging

著者：John Myles Whiteの検索結果

入門 機械学習

Bandit Algorithms for Website Optimization: Developing, Deploying, and Debugging

入門機械学習