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)

Learning about Julia's type system

Our game works like a charm, but there is one thing we can improve—storing our article info as a Dict. Julia's dictionaries are very flexible and powerful, but they are not a good fit in every case. The Dict is a generic data structure that is optimized for search, delete, and insert operations. None of these are needed here—our articles have a fixed structure and contain data that doesn't change once created. It's a perfect use case for objects and object-oriented programming (OOP). Looks like it's time we revisit types.

Julia's type system is the bread and butter of the language—it is all-pervasive, defining the language's syntax and being the driving force behind Julia's performance and flexibility. Julia's type system is dynamic, meaning that nothing is known about types until...