Posted by **AvaxGenius** at July 23, 2017

English | PDF | 2012 | 884 Pages | ISBN : 9380250401 | 87.13 MB

During the last twenty years since the publication of the first edition of the book, the speed as well as memory of computers have increased by a few orders of magnitude. However, the precision with which the floating point operations are being handled on these computers has not improved. As a result, the relevance of roundoff errors in numerical computations has increased substantially. In this book, an attempt is made to demonstrate that with proper care the errors do not grow very fast with the size of computational problem. Thus the book is probably more relevant today than it was twenty years back. The main aim of this book has been to not only provide suitable algorithms for numerical computations, but also to explain their limitations.

Posted by **arundhati** at Aug. 10, 2016

ISBN: 0486652416 | 1986 | EPUB, Djvu | 752 pages | 39 MB

Posted by **DZ123** at April 5, 2014

English | 1962 | ASIN: B0000CLGNH | 427 | pages: 376 | 12,4 mb

Posted by **tarantoga** at June 1, 2013

ISBN: 0486652416 | 1986 | EPUB | 752 pages | 33 MB

Posted by **First1** at Oct. 5, 2017

English | June 6th, 2017 | ASIN: B072MKRQBQ, ISBN: 1491934115 | 193 pages | EPUB | 5.11 MB

Data Science is booming thanks to R and Python, but Java brings the robustness, convenience, and ability to scale critical to today’s data science applications. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. Author Michael Brzustowicz explains the basic math theory behind each step of the data science process, as well as how to apply these concepts with Java.

Posted by **lengen** at July 6, 2017

English | June 22, 2017 | ISBN: 1491934115 | 311 Pages | PDF | 7 MB

Data Science is booming thanks to R and Python, but Java brings the robustness, convenience, and ability to scale critical to today’s data science applications. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. Author Michael Brzustowicz explains the basic math theory behind each step of the data science process, as well as how to apply these concepts with Java.

Posted by **AlenMiler** at June 12, 2017

English | 6 Jun. 2017 | ASIN: B072MKRQBQ | 236 Pages | AZW3 | 2.78 MB

Posted by **leonardo78** at March 17, 2017

1978 | ISBN: 007004452X | 576 pages | PDF + DJVU | (11,5 + 7,1) MB

A clear, practical and self-contained presentation of the methods of asymptotics and perturbation theory for obtaining approximate analytical solutions to differential and difference equations.

Posted by **ChrisRedfield** at July 4, 2014

Published: 2014-02-26 | ISBN: 3319037617 | PDF | 523 pages | 10 MB

Posted by **ChrisRedfield** at May 6, 2014

Published: 1999-12-01 | ISBN: 1441931872, 0387989315 | PDF | 593 pages | 19 MB