An ML introduction to ndarray

Happy RustFest!

It's my pleasure to welcome you to the ML introduction to ndarray workshop!

The material is structured as a series of exercises, or koans, that you can find in the src/koans directory.

You can get started with

git clone [email protected]:LukeMathWalker/ndarray-koans.git
cd ndarray-koans
cargo run

Follow the instructions shown in the terminal to start the first exercise.

Enjoy!

Requirements

Software

• Rust 1.38 (or higher) with cargo
  • Check link for installation instruction if you don't have Rust installed on your machine
  • If you already have Rust installed, run rustc --version to check the version. Run rustup update if you need to update your toolchain (if you installed using rustup)

There are some Jupyter notebooks that you will have to run to perform some data visualisations. Install instructions for those are in python/README.md.

Knowledge

A basic knowledge of Rust is assumed (the first half of the book?). If you run into any issue with the language, please ping me and we'll sort it together!

References

Throughout the workshop, the following resources might turn out to be useful:

• Scientific Computing: A Rust adventure: an introduction to ndarray that assumes no Rust knowledge (not complete yet 😅);
• The Rust Book;
• Docs for the crates in the ndarray ecosystem:
  • ndarray
  • ndarray-rand
  • ndarray-stats
  • ndarray-npy
  • ndarray-linalg
• If you are familiar with Python's NumPy, check ndarray for NumPy users.