Book Image

Julia Programming Projects

By : Adrian Salceanu
Book Image

Julia Programming Projects

By: Adrian Salceanu

Overview of this book

Julia is a new programming language that offers a unique combination of performance and productivity. Its powerful features, friendly syntax, and speed are attracting a growing number of adopters from Python, R, and Matlab, effectively raising the bar for modern general and scientific computing. After six years in the making, Julia has reached version 1.0. Now is the perfect time to learn it, due to its large-scale adoption across a wide range of domains, including fintech, biotech, education, and AI. Beginning with an introduction to the language, Julia Programming Projects goes on to illustrate how to analyze the Iris dataset using DataFrames. You will explore functions and the type system, methods, and multiple dispatch while building a web scraper and a web app. Next, you'll delve into machine learning, where you'll build a books recommender system. You will also see how to apply unsupervised machine learning to perform clustering on the San Francisco business database. After metaprogramming, the final chapters will discuss dates and time, time series analysis, visualization, and forecasting. We'll close with package development, documenting, testing and benchmarking. By the end of the book, you will have gained the practical knowledge to build real-world applications in Julia.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
About Packt

Performance testing

Our code works correctly so far, but what about it's performance? Besides its readable syntax, liberal license, rich package ecosystem, and welcoming community, performance is one of the top reasons why data scientists and software developers choose Julia. The compiler does a great job of providing excellent performance out of the box, but there are certain best practices that we as developers must keep in mind to ensure that we basically don't hinder the compiler. We'll go over the most important ones by looking at a few examples while running some benchmarks.

Benchmarking tools

Given its focus on performance, it should come as no surprise that both core Julia and the ecosystem provide a variety of tools for inspecting our code, looking for bottlenecks and measuring runtime and memory usage. One of the simplest is the @time macro. It takes an expression and then prints its execution time, number of allocations, and the total number of bytes the execution caused to be allocated...