https://github.com/nickgnd/programming-machine-learning-livebooks
Programming Machine Learning - Elixir Livebooks
https://github.com/nickgnd/programming-machine-learning-livebooks
deep-learning elixir livebook machine-learning neural-network nx
Last synced: 6 days ago
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Programming Machine Learning - Elixir Livebooks
- Host: GitHub
- URL: https://github.com/nickgnd/programming-machine-learning-livebooks
- Owner: nickgnd
- License: mit
- Created: 2022-12-05T15:15:40.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-06-02T13:27:18.000Z (almost 2 years ago)
- Last Synced: 2025-04-25T04:54:45.162Z (14 days ago)
- Topics: deep-learning, elixir, livebook, machine-learning, neural-network, nx
- Language: Elixir
- Homepage:
- Size: 12.9 MB
- Stars: 199
- Watchers: 6
- Forks: 18
- Open Issues: 2
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Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-ml-gen-ai-elixir - Programming Machine Learning - Livebook notebooks with code examples for the [Programming Machine Learning book by Paolo Perrotta](https://pragprog.com/titles/pplearn/programming-machine-learning/) (Livebooks & Examples / Development Tools)
README
# Programming Machine Learning: From Coding to Deep Learning - Elixir Livebooks
> Last year (2022), one of my New Year's resolutions was to get into Machine Learning. So, I grabbed a digital copy of this book called ["Programming Machine Learning"](https://pragprog.com/titles/pplearn/programming-machine-learning/) book by [P. Perrotta](https://github.com/nusco) and started my journey. It took a bit longer than I expected, but it was totally worth it in the end.
[Programming Machine Learning](https://pragprog.com/titles/pplearn/programming-machine-learning/) is an hands-on book, it guides you through the creation of an image recognition application from scratch with supervised learning, iteratevely and step-by-step.
All the code examples are in Python/numpy (see [Source Code](https://pragprog.com/titles/pplearn/programming-machine-learning/#resources)), but I decided to give Elixir a spin using Livebook, Nx and company.
## Repository Structure
The repository contains different Livebooks, around one per chapter, and each of them mirrors the corresponding Jupyter Book in the [Source Code](https://pragprog.com/titles/pplearn/programming-machine-learning/#resources) provided with the book.
## Prerequisites
* Erlang and Elixir (https://elixir-lang.org/install.html)
I personally tested it with:
* erlang 25.1.2
* elixir 1.14.2-otp-25
### Livebook
You can install and run [Livebook](https://livebook.dev) in different ways:
* The most convenient one is to download the Livebook Desktop App (https://livebook.dev/#install)
Otherwise:
* via [Docker](https://github.com/livebook-dev/livebook#docker) (the repository has a `docker-compose.yml` file already)
* using [Escript](https://github.com/livebook-dev/livebook#escript) which comes with a convenient CLI
## How to run the livebooks?
* Git clone the repository in your local machine, then:
If you have the Livebook Desktop App installed locally:
* Launch the app and navigate to the cloned repo locally, select one of the livebook and click on the "Open" button.
If you installed Livebook via Escript:
* run `livebook server --home ` (the `home` option will launch Livebook in the repository root folder), then select one of the livebook and click on the "Open" button.
If you want to launch Livebook via Docker:
* run `docker-compose up`, it will launch Livebook in the repository root folder, then select one of the livebook and click on the "Open" button.
## Additional notes
### Differences between Livebook an Jupyter books
* I could replicate all the different Jupyter books in Elixir with Livebook/Nx/Axon, apart from the 2nd section of Chapter 17, where the book introduces L1/L2 regularization techniques and these are not supported by [Axon](https://github.com/elixir-nx/axon) out of the box (more details in the corresponding Livebook).
### Code Style
* The Elixir code style used in the Livebooks is not the most idiomatic one because I aimed to keep it similar and comparable to the Python code in the Jupiter Book.
## Acknowledgements
* First, I want to thank [@nusco](https://github.com/nusco) for the well-written and entertaining book. It has been a pleasant reading and I can only recommend it.
* Then, I want to give a huge shoutout to the elixir-nx and Livebook teams (and contribuitors) for their incredible achievements in less than two years! I'm absolutely blown away by the ecosystem of libraries they've created to make Elixir a possible alternative to the more popular languages in the ML field. The ecosystem is evolving fast and I'm genuily curious to see what the future will bring!
## Contributing
Contributions are welcome! Feel free to open an issue or PR if something is not working as expected or if you see possible improvements ☺️.