title: Scaling up Machine Learning: Parallel and Distributed Approaches
author: Ron Bekkerman
description: "One of the landmark achievements of our time is the ability to extract value from large volumes of data. Engineering and algorithmic developments on this front have gelled substantially in recent years, and are quickly being reduced to practice in widely-available, reusable forms. This book provides a broad and timely snapshot of the state of developments in scalable machine learning, which should be of interest to anyone who wishes to understand and extend the state of the art in analyzing data." <br />Joseph M. Hellerstein, University of California, Berkeley<br /><br />"This is a book that every machine learning practitioner should keep in their library." <br />Yoram Singer, Google Inc.<br /><br />"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." <br />Joydeep Ghosh, University of Texas<br /><br />"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." <br />William W. Cohen, Carnegie Mellon University<br /><br />"... an excellent resource for researchers in the field." <br />J. Arul for Computing Reviews
zlib_text: www.allitebooks.com
  
  Scaling Up Machine Learning
  Parallel and Distributed Approaches
  This book comprises a collection of representative approaches for scaling up machine learn-
  ing and data mining methods on parallel and distributed computing platforms. Demand for
  parallelizing learning algorithms is highly task-speciﬁc: in some settings it is driven by the
  enormous dataset sizes, in others by model complexity or by real-time performance require-
  ments. Making task-appropriate algorithm and platform choices for large-scale machine
  learning requires understanding the beneﬁts, trade-offs, and constraints of the available
  options.
  Solutions presented in the book cover a range of parallelization platforms from FPGAs
  and GPUs to multi-core systems and commodity clusters; concurrent programming frame-
  works that include CUDA, MPI, MapReduce, and DryadLINQ; and various learning set-
  tings: supervised, unsupervised, semi-supervised, and online learning. Extensive coverage
  of parallelization of