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商品描述
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
商品描述(中文翻譯)
當尋找改善網站的方法時，您如何決定要進行哪些更改？又該保留哪些更改？這本簡明的書籍向您展示如何使用多臂賭徒演算法來衡量您對網站所做的任何修改的實際價值。作者 John Myles White 向您展示這種強大的演算法類別如何幫助您提升網站流量、將訪客轉換為客戶，並增加許多其他成功指標。
這是第一本專注於賭徒演算法的開發者書籍，這些演算法之前僅在研究論文中描述。您將通過使用 Python 編寫的程式碼範例快速學習幾種簡單演算法的好處，包括 epsilon-Greedy、Softmax 和 Upper Confidence Bound (UCB) 演算法，這些範例您可以輕鬆調整以便在自己的網站上部署。
- 學習 A/B 測試的基本原理，並認識到何時使用賭徒演算法更為合適
- 開發一個單元測試框架以調試賭徒演算法
- 獲取用 Julia、Ruby 和 JavaScript 編寫的額外程式碼範例及補充的線上材料
