# Download Parallel Computing for Data Science: With Examples in R, C++ and CUDA (Chapman & Hall/CRC The R Series) eBook

## by **Norman Matloff**

Probability and Statistics for Data Science (Chapman & Hall/CRC Data Science Series). From my reading of the book, Matloff achieves his goals, and in doing so he has provided a volume that will be immensely useful to a very wide audience

Probability and Statistics for Data Science (Chapman & Hall/CRC Data Science Series). From my reading of the book, Matloff achieves his goals, and in doing so he has provided a volume that will be immensely useful to a very wide audience.

Chapman and Hall/CRC Published June 4, 2015 Reference - 328 Pages - 7 B/W Illustrations ISBN . This is a book that I will use, both as a reference and for instruction. The examples are poignant and the presentation moves the reader directly from concept to working code.

Chapman and Hall/CRC Published June 4, 2015 Reference - 328 Pages - 7 B/W Illustrations ISBN 9781466587014 - CAT K20322 Series: Chapman & Hall/CRC The R Series. eBooks are subject to VAT, which is applied during the checkout process. What are VitalSource eBooks? Chapman and Hall/CRC Published June 4, 2015 Reference - 328 Pages ISBN 9780429072161 - CAT KE75541. Michael Kane, Yale University.

Series: Chapman & Hall/CRC the R series (CRC Press). Other readers will always be interested in your opinion of the books you've read. File: PDF, . 4 MB. Save for later. Whether you've loved the book or not, if you give your honest and detailed thoughts then people will find new books that are right for them.

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Parallel Computing for Data Science: With Examples in R, C++ and CUDA Chapman & Hall/CRC The R Series (Том 28). Автор.

Parallel Computing for Data Science: With Examples in R, C++ and CUDA is one of the first parallel computing books to concentrate exclusively on parallel data structures, algorithms, software tools, and applications in data science. Dr. Norman Matloff is a professor of computer science at the University of California, Davis, where he was a founding member of the Department of Statistics. He is a statistical consultant and a former database software developer. Parallel Computing for Data Science: With Examples in R, C++ and CUDA Chapman & Hall/CRC The R Series (Том 28).

first parallel computing books to concentrate exclusively on parallel data structures, algorithms, software tools, and applications in data science. The examples illustrate the range of issues encountered in parallel programming. It includes examples not only from the classic n observations, p variables matrix format but also from time series, network graph models, and numerous other structures common in data science.

Chapman and Hall/CRC. 328 pages 7 B/W Illus. Example: Finding the Maximal Burst in a Time Series. Example: Transformation of an Adjacency Matrix. For Instructors Request Inspection Copy. Dirk Eddelbuettel, Debian and R Projects. Example: k-Means Clustering. It includes examples not only from the classic "n observations, p variables" matrix format but also from time series, network graph models, and numerous other structures common in data science. Xie and Matloff (2014) proposed remedying this problem by plotting only the most patterns, with frequency defined in terms of nonparametrically estimated multivariate density.

Parallel Computing for Data Science: With Examples in R, C++ and CUDA is one of the first parallel computing books to concentrate exclusively on parallel data structures, algorithms, software tools, and applications in data science. It includes examples not only from the classic "n observations, p variables" matrix format but also from time series, network graph models, and numerous other structures common in data science. The examples illustrate the range of issues encountered in parallel programming.

With the main focus on computation, the book shows how to compute on three types of platforms: multicore systems, clusters, and graphics processing units (GPUs). It also discusses software packages that span more than one type of hardware and can be used from more than one type of programming language. Readers will find that the foundation established in this book will generalize well to other languages, such as Python and Julia.