Book Image

Julia 1.0 Programming Complete Reference Guide

By : Ivo Balbaert, Adrian Salceanu
Book Image

Julia 1.0 Programming Complete Reference Guide

By: Ivo Balbaert, Adrian Salceanu

Overview of this book

Julia offers the high productivity and ease of use of Python and R with the lightning-fast speed of C++. There’s never been a better time to learn this language, thanks to its large-scale adoption across a wide range of domains, including fintech, biotech and artificial intelligence (AI). You will begin by learning how to set up a running Julia platform, before exploring its various built-in types. This Learning Path walks you through two important collection types: arrays and matrices. You’ll be taken through how type conversions and promotions work, and in further chapters you'll study how Julia interacts with operating systems and other languages. You’ll also learn about the use of macros, what makes Julia suitable for numerical and scientific computing, and how to run external programs. Once you have grasped the basics, this Learning Path goes on to how to analyze the Iris dataset using DataFrames. While building a web scraper and a web app, you’ll explore the use of functions, methods, and multiple dispatches. In the final chapters, you'll delve into machine learning, where you'll build a book recommender system. By the end of this Learning Path, you’ll be well versed with Julia and have the skills you need to leverage its high speed and efficiency for your applications. This Learning Path includes content from the following Packt products: • Julia 1.0 Programming - Second Edition by Ivo Balbaert • Julia Programming Projects by Adrian Salceanu
Table of Contents (18 chapters)

Performance tips

Throughout this book, we have paid attention to performance. Here, we summarize some highlighted performance topics and give some additional tips. These tips need not always be used, and you should always benchmark or profile the code and the effect of a tip. However, applying some of them can often yield a remarkable performance improvement. Using type annotations everywhere is certainly not the way to go; Julia's type inferring engine does that work for you:

  • Refrain from using global variables. If unavoidable, make them constant with const, or at least annotate the types. It is better to use local variables instead; they are often only kept on the stack (or even in registers), especially if they are immutable.
  • Use a main() function to structure your code.
  • Use functions that do their work on local variables via function arguments, rather than mutating...