Book Image

Julia 1.0 Programming Cookbook

By : Bogumił Kamiński, Przemysław Szufel
Book Image

Julia 1.0 Programming Cookbook

By: Bogumił Kamiński, Przemysław Szufel

Overview of this book

Julia, with its dynamic nature and high-performance, provides comparatively minimal time for the development of computational models with easy-to-maintain computational code. This book will be your solution-based guide as it will take you through different programming aspects with Julia. Starting with the new features of Julia 1.0, each recipe addresses a specific problem, providing a solution and explaining how it works. You will work with the powerful Julia tools and data structures along with the most popular Julia packages. You will learn to create vectors, handle variables, and work with functions. You will be introduced to various recipes for numerical computing, distributed computing, and achieving high performance. You will see how to optimize data science programs with parallel computing and memory allocation. We will look into more advanced concepts such as metaprogramming and functional programming. Finally, you will learn how to tackle issues while working with databases and data processing, and will learn about on data science problems, data modeling, data analysis, data manipulation, parallel processing, and cloud computing with Julia. By the end of the book, you will have acquired the skills to work more effectively with your data
Table of Contents (18 chapters)
Title Page
Copyright and Credits
About Packt


Julia is shipped with a vast array of utility functions built into the core language and its standard libraries. Also, because of its speed, it is very well suited for the implementation of custom algorithms (as opposed to many other high-level languages, where users compose the analysis solely by using predefined algorithms from installed packages).

In this chapter, we show practical examples of how such custom algorithms can be implemented, while also taking advantage of the inbuilt functionality. The range of recipes shows that you can often implement your own low-level algorithm that is much faster than using standard functions (the recipe for finding the index of a random minimum element in an array) and that you can easily modify how standard operations work by overriding them with custom behavior (the matrix multiplication optimization recipe). Finally, we discuss in the recipes how the most frequently used data structures and operations can be effectively used, concentrating...