Julia 1.0 Programming Complete Reference Guide

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  • Create your own types to extend the built-in type system
  • Visualize your data in Julia with plotting packages
  • Explore the use of built-in macros for testing and debugging
  • Integrate Julia with other languages such as C, Python, and MATLAB
  • Analyze and manipulate datasets using Julia and DataFrames
  • Develop and run a web app using Julia and the HTTP package
  • Build a recommendation system using supervised machine learning

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
  • Leverage Julia's high speed and efficiency to build fast, efficient applications
  • Perform supervised and unsupervised machine learning and time series analysis
  • Tackle problems concurrently and in a distributed environment
Page Count 466
Course Length 13 hours 58 minutes
ISBN 9781838822248
Date Of Publication 19 May 2019


Ivo Balbaert

Ivo Balbaert has been a lecturer in web programming and databases at CVO Antwerpen, a community college in Belgium. He received a Ph.D. in applied physics from the University of Antwerp in 1986. He worked for 20 years in the software industry as a developer and consultant in several companies, and for 10 years as project manager at the University Hospital of Antwerp. From 2000 onwards, he switched to partly teaching and partly developing software (at KHM Mechelen, CVO Antwerpen). He also wrote an introductory book in Dutch about developing in Ruby and Rails, Programmeren met Ruby en Rails, by Van Duuren Media. In 2012, he authored a book on the Go programming language, The Way To Go, by IUniverse. He wrote a number of introductory books for new programming languages, notably Dart, Julia, Rust, and Red, all published by Packt.

Adrian Salceanu

Adrian Salceanu has been a professional software developer for over 15 years. For the last 10 years, he has been leading agile teams in developing real-time, data-intensive web and mobile products. Adrian is a public speaker and an enthusiastic contributor to the open source community, focusing on high-performance web development. He is the organizer of the Barcelona Julia Users group and the creator of Genie, a high-performance, highly productive Julia web framework. Adrian has a master's degree in computing and a postgraduate degree in advanced computer science.