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HIGHLIGHT
The first and only book on Apache Mahout, an open source tool for
leveraging machine learning techniques in large-scale applications.
DESCRIPTION
To benefit from prior experience in the use of a website, machine
learning techniques are increasingly used. The Apache Mahout project
is focused on three types of machine learning that are of particular
interest to modern web developers—recommendation systems,
classification, and clustering.
Through real-world examples, Mahout in Action introduces the sorts of
problems that these techniques are appropriate for, and then illustrates
how Mahout can be applied to solve them. It places particular focus on
issues of scalability, and how to apply these techniques at very large
scale with the Apache Hadoop framework.
KEY POINTS
This book assumes familiarity with Java, and some basic grounding in
machine learning techniques.
F• First and only book devoted to Apache Mahout
F• Practical insights from industry practitioners
F• Real-world examples
F• Discussion of large-scale implemetation with Hadoop
Sean Owen
has been a practicing software engineer for 9 years, most
recently at Google, where he helped build and launch Mobile Web search.
He joined Apache's Mahout machine learning project in 2008 as a primary
committer and works as a Mahout consultant.
Robin Anil is a committer at Mahout and works as a full-time Software
Engineer at Google.
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Frais de port :
EUR 3,73
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Description du livre Etat : New. The Apache Mahout project is focused on three types of machine learning: recommendation systems, classification, and clustering. This text introduces the sorts of problems that these techniques are appropriate for, and then illustrates how Mahout can be applied to solve them. Num Pages: 375 pages, Illustrations. BIC Classification: UMB; UMX. Category: (P) Professional & Vocational. Dimension: 233 x 189 x 21. Weight in Grams: 706. . 2011. Pap/Psc. Paperback. . . . . N° de réf. du vendeur V9781935182689
Description du livre Etat : New. The Apache Mahout project is focused on three types of machine learning: recommendation systems, classification, and clustering. This text introduces the sorts of problems that these techniques are appropriate for, and then illustrates how Mahout can be applied to solve them. Num Pages: 375 pages, Illustrations. BIC Classification: UMB; UMX. Category: (P) Professional & Vocational. Dimension: 233 x 189 x 21. Weight in Grams: 706. . 2011. Pap/Psc. Paperback. . . . . Books ship from the US and Ireland. N° de réf. du vendeur V9781935182689