Apache.Mahout.Beyond.MapReduce.1523775785

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