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)

Implementing Recommender Systems with Julia

In the previous chapters, we took a deep dive into data mining and web development with Julia. I hope you enjoyed a few relaxing rounds of Six Degrees of Wikipedia while discovering some interesting articles. Randomly poking through the millions of Wikipedia articles as part of a game is a really fun way to stumble upon interesting new content. Although I'm sure that, at times, you've noticed that not all the articles are equally good—maybe they're stubs, or subjective, or not so well written, or simply irrelevant to you. If we were able to learn about each player's individual interests, we could filter out certain Wikipedia articles, which would turn each game session into a wonderful journey of discovery.

It turns out that we're not the only ones struggling with this—information discovery...