Sprache: Englisch
Verlag: O'Reilly Media, Incorporated, 2012
ISBN 10: 1449341330 ISBN 13: 9781449341336
Anbieter: Better World Books, Mishawaka, IN, USA
Zustand: Very Good. Used book that is in excellent condition. May show signs of wear or have minor defects.
Anbieter: WeBuyBooks, Rossendale, LANCS, Vereinigtes Königreich
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Softcover. Zustand: Bon. Petite(s) trace(s) de pliure sur la couverture. Pages cornées. Edition 2012. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Good. Slightly creased cover. Dog-eared pages. Edition 2012. Ammareal gives back up to 15% of this item's net price to charity organizations.
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In den WarenkorbPaperback. Zustand: Brand New. 1st edition. 73 pages. 9.00x7.00x0.50 inches. In Stock.
Zustand: New. 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.When looking for ways to improve your website, how do you decide which.
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Neuware - 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.