Posted by **insetes** at April 11, 2019

2012 | 67 Pages | ISBN: 1449305466 | PDF | 7 MB

Posted by **lengen** at April 22, 2019

English | 2017 | ISBN: N/A | 345 Pages | PDF | 30.7 MB

This book consists of a set of is tutorials on the scientific Python ecosystem: a quick introduction to central tools and techniques. The different chapters each correspond to a 1 to 2 hours course with increasing level of expertise, from beginner to expert.

Posted by **Sigha** at Nov. 22, 2018

.MP4 | Video: 1280x720, 30 fps(r) | Audio: AAC, 44100 Hz, 2ch | 4.52 GB

Duration: 10 hours | Genre: eLearning | Language: English

Learn SciPy Library in detail with Python 3, Jupyter, NumPy, and Matplotlib

Posted by **AlenMiler** at June 21, 2018

English | 2018 | ASIN: B07DW2X4MJ | 189 Pages | MOBI | 1.34 MB

Posted by **AlenMiler** at July 11, 2018

English | 20 Dec. 2017 | ISBN: 1788291468 | 386 Pages | EPUB | 4.77 MB

Posted by **First1** at Oct. 24, 2018

English | January 5th, 2018 | ISBN: 1788291468 | 386 Pages | EPUB (True/HQ) | 12.19 MB

Tackle the most sophisticated problems associated with scientific computing and data manipulation using SciPy

Posted by **roxul** at June 4, 2016

English | ISBN: 1783987707 | 2015 | 136 pages | EPUB, True PDF | 7 MB

Posted by **interes** at Sept. 25, 2016

English | ISBN: 1783987707 | 2015 | 136 pages | PDF | 3,5 MB

Posted by **FenixN** at Jan. 3, 2017

HDRips | MP4/AVC, ~1012 kb/s | 1280x720 | Duration: 00:52:30 | English: AAC, 128 kb/s (2 ch) | 288 MB

Computational computing can be a complex topic. How to perform various mathematical functions in code isn't straight forward. With Python's Scipy library, we'll walk through a number of examples showing exactly how to create and execute complex computational computing functions.

Posted by **naag** at April 4, 2017

MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 3 Hours 38M | 751 MB

The SciPy Stack is a collection of Open-Source Python libraries finding their application in many areas of technical and scientific computing. It builds on the capabilities of the NumPy array object for faster computations, and contains modules and libraries for linear algebra, signal and image processing, visualization, and much more. Accordingly, gaining a solid working knowledge on some of the basic functionality of the SciPy Stack to solve mathematical models numerically is clearly the first step before one can start using it to tackle large-scale computational projects either in the industry or in the academic world.