Demos for Book
Overview
https://github.com/JeffFessler/book-la-demo
Demos in the Julia language, compiled using Literate and Documenter to accompany the 2024 book Linear Algebra for Data Science, Machine Learning, and Signal Processing by Jeff Fessler and Raj Nadakuditi at the University of Michigan.
Getting started with Julia
Install Julia from https://julialang.org
Launch the Julia app; it should open a Julia REPL.
To develop code, select an editor, preferably with Julia integration, such as VSCode or vim perhaps with tmux. Appropriate editor plug-ins are needed to use LaTeX-like tab-completion of unicode characters like
÷ ⊗ ⊕ ∘ ×andα β γ.Peruse the demos listed in the menu here. If your browser window is wide enough, you should see a menu to the left. If your window is narrow, you should see a ☰ hamburger menu button that will toggle open the demos menu sidebar.
Check out some Julia tutorials, especially the one titled "Just the Julia you need to get started in Data Science and ML" by Raj Rao.
More resources
Install the web browser shortcut for fast access to the online Julia manual.
Use the package AbbreviatedStackTraces.jl to get more interpretable error messages.
For image processing, view the excellent documentation at JuliaImages
For a machine learning introduction, see the Julia programming for Machine Learning course material.
Getting started with Julia for matrix methods
These examples show you Julia code and the corresponding output in an HTML format suitable for viewing in a web browser without installing any software.
You could cut and paste portions of that Julia code into the Julia REPL, but that becomes tedious. Instead, click on the "Edit on GitHub" link (in the upper right, with github icon), where you can then download the entire Julia code file that generated any of these examples.
For example, the code for the SVD demo is at this url. After downloading such a file such as svd-demo.jl, you can run it by typing include("svd-demo.jl") at the Julia REPL.