The Julia programming language can be used to write all types of applications, but its features are especially powerful for numerical analysis and computational science. Practical Julia shows readers how to take advantage of Julia's particular strengths, as well as how to write effective and efficient programs.
The book takes Julia novices from their very first steps to writing real-world applications for use in fields such as biology, physics, math, statistics, and machine learning. Not only will readers develop the Julia knowledge needed for solving computational problems, but they'll also learn how to explore and visualize data, solve equations, write simulations, and create libraries. Additional online resources include ready-to-run code samples, illustrations, and supplemental animations.
Introduction
Part I: Learning Julia
1. Getting Started
2. Language Basics
3. Using Modules
4. The Plotting System
5. Collections
6. Functions, Metaprogramming, and Errors
7. Diagrams and Animations
8. The Type System
Part II: Applications
9. Physics
10. Statistics
11. Biology
12. Mathematics
13. Scientific Machine Learning
14. Signal Processing
15. Parallel Processing
Bibliography
Index
The chapters in red are included in this Early Access PDF.
"This is a nice deep dive that covers a lot of ground, from the basics on how to define arrays and use the type system all the way to biochemical modeling and scientific machine learning. Lee gives a very nice in-depth treatment, showing not only the most standard ways to do things, but also some different library options along with a good explanation of the pros and cons to the choices. I think this is a great book for any Julia user's shelf."
—Christopher Rackauckas, Applied Mathematics Instructor, Massachusetts Institute of Technology
