https://github.com/lukemathwalker/ndarray-koans

Material for "ML Introduction to ndarray" workshop at RustFest 2019.
https://github.com/lukemathwalker/ndarray-koans

Last synced: 2 months ago
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Material for "ML Introduction to ndarray" workshop at RustFest 2019.

• Host: GitHub
• URL: https://github.com/lukemathwalker/ndarray-koans
• Owner: LukeMathWalker
• License: apache-2.0
• Created: 2019-10-13T10:52:16.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2021-03-20T02:06:52.000Z (almost 4 years ago)
• Last Synced: 2024-10-14T14:49:20.164Z (3 months ago)
• Language: Jupyter Notebook
• Homepage: https://barcelona.rustfest.eu/sessions/machine-learning-ndarray
• Size: 363 KB
• Stars: 111
• Watchers: 4
• Forks: 20
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# 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

```bash

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](https://www.rust-lang.org/tools/install) 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](https://doc.rust-lang.org/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](https://www.lpalmieri.com/posts/2019-02-23-scientific-computing-a-rust-adventure-part-0-vectors/): an

  introduction to `ndarray` that assumes no Rust knowledge (not complete yet 😅);

* The [Rust Book](https://doc.rust-lang.org/book/);

* Docs for the crates in the `ndarray` ecosystem:

    * [`ndarray`](https://docs.rs/ndarray/0.13.0/ndarray/)

    * [`ndarray-rand`](https://docs.rs/ndarray-rand/0.11.0/ndarray_rand/)

    * [`ndarray-stats`](https://docs.rs/ndarray-stats/0.3.0/ndarray/)

    * [`ndarray-npy`](https://docs.rs/ndarray-npy/0.5.0/ndarray_npy/)

    * [`ndarray-linalg`](https://docs.rs/ndarray-linalg/0.12.0/ndarray_linalg/)

* If you are familiar with Python's `NumPy`, check [`ndarray` for `NumPy` users](https://docs.rs/ndarray/0.13.0/ndarray/doc/ndarray_for_numpy_users/index.html).