Scaling up Machine Learning: Parallel and Distributed Approaches

“This is a book that every machine learning practitioner should keep in their library.”
Yoram Singer, Google Inc.

“This unique, timely book provides a 360 degrees view and understanding of both conceptual and practical issues that arise when implementing leading machine learning algorithms on a wide range of parallel and high-performance computing platforms. It will serve as an indispensable handbook for the practitioner of large-scale data analytics and a guide to dealing with BIG data and making sound choices for efficient applying learning algorithms to them. It can also serve as the basis for an attractive graduate course on Parallel/Distributed Machine Learning and Data Mining.”
Joydeep Ghosh, University of Texas

“The contributions in this book run the gamut from frameworks for large-scale learning to parallel algorithms to applications, and contributors include many of the top people in this burgeoning subfield. Overall this book is an invaluable resource for anyone interested in the problem of learning from and working with big datasets.”
William W. Cohen, Carnegie Mellon University

“… an excellent resource for researchers in the field.”
J. Arul for Computing Reviews