Posted by **AvaxGenius** at March 25, 2017

English | PDF | 2017 | 300 Pages | ISBN : 3319502239 | 4.48 MB

This textbook is designed with the needs of today’s student in mind. It is the ideal textbook for a first course in elementary differential equations for future engineers and scientists, including mathematicians.

Posted by **ChrisRedfield** at Nov. 21, 2015

Published: 2013-05-22 | ISBN: 1461472962, 1461472989 | PDF | 263 pages | 2.71 MB

Posted by **nebulae** at Oct. 8, 2013

English | ISBN: 1461472962 | 2013 | 280 pages | PDF | 3 MB

Posted by **roxul** at March 6, 2015

English | ISBN: 0199208255, 0199208247 | 2007 | 544 pages | PDF | 6 MB

Posted by **nebulae** at May 29, 2014

English | ISBN: 0199208255, 0199208247 | 2007 | 544 pages | PDF | 6 MB

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 **libr** at July 10, 2017

English | 2011 | ISBN: 0313391734 , 0313391947 | 225 pages | PDF | 1,2 MB

Posted by **roxul** at July 7, 2017

2012 | ISBN-10: 0321740904 | 1375 pages | PDF | 41 MB

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 **roxul** at June 21, 2017

1999 | ISBN-10: 9810236026 | 244 pages | PDF, Djvu | 8 + 2 MB