Apache Mahout: Beyond MapReduce

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 conceptual approaches to attacking Machine Learning problems in big datasets. Math is explained, followed by code examples of distributed and in-memory computations.

Written by Apache Mahout committers for people who want to learn how to design distributed math algorithms as well as how to use some of the new Mahout “Samsara” algorithms off-the-shelf.

The book covers Apache Mahout 0.10 and 0.11.

Table of Contents

Part I — First steps
Chapter 1 Meet Mahout
Chapter 2 Setting things up

Part II — Coding with Mahout
Chapter 3 In-core Algebra
Chapter 4 Distributed Algebra

Part III — Approximating Distributed Problems
Chapter 5 Stochastic SVD
Chapter 6 Stochastic PCA
Chapter 7 Data Sketching with Bahmani sketch

Part IV — Samsara Tutorials
Chapter 8 Naive Bayes Example

Appendix A Mahout Book Conventions
Appendix B In-core Algebra Reference
Appendix C Distributed Algebra Reference