共17章，287页。 1. Meet Apache Mahout Part 1 Recommendations 2. Introducing recommenders 3. Representing data 4. Making recommendations 5. Taking recommenders to production 6. Distributing recommendation computations Part 2 Clustering 7. Introduction to
Mahout in Action 3. Representing data 4. Making recommendations 5. Taking recommenders to production 6. Distributing recommendation computations Part 2 Clustering 7. Introduction to clustering 8. Representing data 9. Clustering algorithms in Mahout
If you are a Java developer and want to use Mahout and Machine Learning to solve Big Data analytics use-cases then this book is for you. Familiarity with shell-scr ipts is assumed but no prior experience is required. Table of Contents Chapter 1: Int
Apache Mahout: Beyond MapReduce. Distributed algorithm design This book is about designing mathematical and Machine Learning algorithms using the Apache Mahout "Samsara" platform. The material takes on best programming practices as well as conceptua
Key Features This book is based on the latest 2.0 version of Apache Spark and 2.7 version of Hadoop integrated with most commonly used tools. Learn all Spark stack components including latest topics such as DataFrames, DataSets, GraphFrames, Structu
