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)

Floating point numbers

Floating point numbers follow the IEEE 754 standard and represent numbers with a decimal point, such as 3.14, or an exponent notation, such as 4e-14, and come in the types Float16 up to Float64, the last one being used for double precision.

Single precision is achieved through the use of the Float32 type. Single precision float literals must be written in scientific notation, such as 3.14f0, but with f, where one normally uses e. That is, 2.5f2 indicates 2.5*10^2 with single precision, while 2.5e2 indicates 2.5*10^2 in double precision. Julia also has a BigFloat type for arbitrary-precision floating numbers computations.

A built-in type promotion system takes care of all the numeric types that can work together seamlessly, so that there is no explicit conversion needed. Special values exist: Inf and -Inf are used for infinity, and NaN...