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

by Norman Matloff

Download Parallel Computing for Data Science: With Examples in R, C++ and CUDA (Chapman & Hall/CRC The R Series) eBook
ISBN:
1466587016
Author:
Norman Matloff
Category:
Mathematics
Language:
English
Publisher:
Chapman and Hall/CRC; 1 edition (June 4, 2015)
Pages:
328 pages
EPUB book:
1753 kb
FB2 book:
1633 kb
DJVU:
1711 kb
Other formats
azw lrf lit doc
Rating:
4.4
Votes:
884


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.

  • Rindyt
The basic idea of the book is good and such a book is very timely. Matloff delivers with clarity and authority, which is good. The book suffers a lot, however, by the lack of a good professional edit. The leading example, though useful in containing many the features you would need as an illustrative example is, frankly, rather pedestrian and why anyone would want to do such a calculation is left unmotivated. The code listings in the book, of which there are a great number of course, are presented in a variable pitch roman font making them not only unattractive, but difficult to read quickly. It's very jarring. Worse still, though, is the large number of typos and other infelicities that mean you have to check constantly as you read. The book shows every sign of being published in a great rush, and it almost looks like the author is leaning on his readers to do the final edit! The book also has no Bibliography and the only inline references I could find were to the author's own previous book on The Art of R Programming.
  • Sudert
Let me preface by saying that I consider Matloff's "The Art of R Programming" to be the best book on R available. So, I had very high hopes for this book. However, this book falls far short of the quality of Matloff's book on R.

In general, I greatly agree with Venables prior review on editing and typesetting. It appears that the printing was done in the basic font used in LaTeX documents made in RStudio or a basic LaTeX editor. In my mind, Matloff was done a huge disservice by CRC Press by not doing a professional edit and making appropriate typesetting decisions.

Moving to the book's content, I found the information very informative and useful. In particular Chapters 2 - 6 were great.

That said, given how much of this information is freely available online, it's hard to justify a purchase. In addition, this edition has changed very little from the rough draft that is freely available on Dr Matloff's website. Given the poor quality edit and the lack of new / revised material vs the free draft version, I have a hard time recommending this as a purchase, though I do consider it a valuable reference. Yes, I realize that that is seemingly contradictory.
  • Jogas
Not worth the money, especially since there are so many good parallel materials out there for free. The book felt rushed too, like it wasn't proofread enough times or something.
  • Malakelv
This book is for people who have no prior experience of parallel programming so it explains basic concepts well. I didn't read it thoroughly looking for mistakes. I read it to get an understanding of parallel programming at C level in the context of R. And for that, I cannot fault this book. It was my go-to reference for my first parallel C effort: data.table::fwrite(). I then moved on to free online articles after that when I became more advanced. But I needed this book as a grounding first.