https://github.com/bpesquet/mlkatas

(Phased out) A series of challenges for practicing your Machine Learning and Deep Learning skills
https://github.com/bpesquet/mlkatas

activity assignment challenge exercise kata keras machine-learning nbgrader numpy python scikit-learn

Last synced: 3 months ago
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(Phased out) A series of challenges for practicing your Machine Learning and Deep Learning skills

• Host: GitHub
• URL: https://github.com/bpesquet/mlkatas
• Owner: bpesquet
• License: other
• Created: 2018-09-20T08:11:15.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2024-03-22T20:29:35.000Z (about 1 year ago)
• Last Synced: 2025-01-31T02:22:44.835Z (3 months ago)
• Topics: activity, assignment, challenge, exercise, kata, keras, machine-learning, nbgrader, numpy, python, scikit-learn
• Language: Jupyter Notebook
• Homepage: https://www.bpesquet.fr/mlkatas/
• Size: 19.3 MB
• Stars: 68
• Watchers: 7
• Forks: 19
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# ⛩ Machine Learning Katas

> This project is being phased out and replaced by [ainotes](https://github.com/bpesquet/ainotes).

This repository contains the source files for the [Machine Learning Katas](https://www.bpesquet.fr/mlkatas), a series of challenges for practicing your Machine Learning and Deep Learning skills.

## Usage

The katas are mostly self-correcting [Jupyter Notebooks](https://jupyter.org/) that can be executed either:

- online, by accessing the [katas website](https://bpesquet.github.io/mlkatas/).

- locally, by cloning or downloading this repository then spinning up a Jupyter notebook server on your local machine.

## Development notes

### Adding katas

The katas are generated by [nbgrader](https://nbgrader.readthedocs.io) from completed versions that live in a separate, private repository.

### Generating the site

The [website](https://www.bpesquet.fr/mlkatas) is generated by [Jupyter Book](https://jupyterbook.org). After installing it, execute the following command in the root folder to generate the HTML output in the `_build` subdirectory:

```bash

jupyter-book build .

```

A [GitHub action](.github/workflows/deploy.yaml) is used to publish this output as a website.