具體描述
This book shows you how to run experiments on your website using A/B testing - and then takes you a huge step further by introducing you to bandit algorithms for website optimization. Author John Myles White shows you how this family 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 have previously only been described in research papers. You'll learn about several simple algorithms you can deploy on your own websites to improve your business including the epsilon-greedy algorithm, the UCB algorithm and a contextual bandit algorithm. All of these algorithms are implemented in easy-to-follow Python code and be quickly adapted to your business's specific needs. You'll also learn about a framework for testing and debugging bandit algorithms using Monte Carlo simulations, a technique originally developed by nuclear physicists during World War II. Monte Carlo techniques allow you to decide whether A/B testing will work for your business needs or whether you need to deploy a more sophisticated bandits algorithm.
用戶評價
##比論文簡單，比網上的文章全麵，還算比較實用
評分##多臂賭博機問題入門，容易上手，但都比較淺顯
評分 評分##多臂賭博機問題入門，容易上手，但都比較淺顯
評分##多臂老虎機的入門介紹小冊子，可以翻一翻。但是作者代碼不太好，有個bug貫穿始終，導緻他發現瞭不該發現的特徵。白璧微瑕。 multiarmed bandit原本是從賭場中的多臂老虎機的場景中提取齣來的數學模型。 是無狀態（無記憶）的reinforcement learning。目前應用在operation research，機器人，網站優化等領域。 arm：指的是老虎機 （slot machine）的拉杆。 bandit：多個拉杆的集閤，bandit = {arm1, ar...
評分 評分##多臂賭博機問題入門，容易上手，但都比較淺顯
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