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Learning Julia

You're reading from   Learning Julia Build high-performance applications for scientific computing

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781785883279
Length 316 pages
Edition 1st Edition
Languages
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Authors (2):
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Rahul Lakhanpal Rahul Lakhanpal
Author Profile Icon Rahul Lakhanpal
Rahul Lakhanpal
Anshul Joshi Anshul Joshi
Author Profile Icon Anshul Joshi
Anshul Joshi
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Table of Contents (11) Chapters Close

Preface 1. Understanding Julia's Ecosystem FREE CHAPTER 2. Programming Concepts with Julia 3. Functions in Julia 4. Understanding Types and Dispatch 5. Working with Control Flow 6. Interoperability and Metaprogramming 7. Numerical and Scientific Computation with Julia 8. Data Visualization and Graphics 9. Connecting with Databases 10. Julia’s Internals

Linear algebra and differential calculus

Julia is targeted toward the scientific community. Starting with this topic, we will see how Julia greatly eases mathematical calculations in the real world. We will start off by explaining how Julia proves to be a great resource for solving problems in linear algebra.

Linear algebra

The syntax used in Julia closely resembles that of MATLAB, but there are some important differences. To begin with, look at the matrix of some randomly generated numbers:

julia> A = rand(3,3)
3×3 Array{Float64,2}:
 0.821807   0.828687  0.974031
 0.996824   0.805663  0.274284
 0.0341033  0.224237  0.39982

The rand function takes in parameters asking for the dimensions of the array...

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