3 Review(s)

John Myles White’s Bandit Algorithms for Website Optimization, published by O’Reilly, is a comprehensive book on developing, deploying and debugging the efficient website optimization algorithms to improve the quality of websites. The book eloquently discusses how these algorithms could increase website traffic, help to turn the visitors into customers and how to boost up the other success measures of a website. It has many code examples written in Python, which is effortlessly adaptable for deployment on websites. The book consists of detailing on Greedy, Softmax and Upper Confidence Bound algorithms. About John Myles White John Myles White works for Facebook a Data Scientist. He had previously worked as Julia Developer at the Massachusetts Institute of Technology. He graduated from the Princeton University and is currently pursuing his PhD there. John is also the chief maintainer for many popular R packages like ProjectTemplate and log4r. He is also the author of Machine Learning for Hackers and Machine Learning for Email: Spam Filtering and Priority Inbox. About O’Reilly O’Reilly is a renowned media house which has been publishing technology books, tech conferences, IT courses and news, online services, magazines and research since the year 1978. Some of the books published by O’Reilly are Lean UX Workshop, Software Architecture Fundamentals Part 2: Taking a Deeper Dive, Bioinformatics Data Skills, Head First JavaScript Programming and Python Networking Programming Cookbook.