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Add binder info to README.
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jatkinson1000 committed May 9, 2024
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![GitHub](https://img.shields.io/github/license/Cambridge-ICCS/ml-training-material)
[![CC BY-NC-SA 4.0][cc-by-nc-sa-shield]][cc-by-nc-sa]
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/Cambridge-ICCS/ml-training-material/main)

This repository contains documentation, resources, and code for the Introduction to
Machine Learning with PyTorch session designed and delivered by [Jack Atkinson](https://jackatkinson.net/) ([**@jatkinson1000**](https://github.com/jatkinson1000))
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## Installation and setup

There are two options for participating in this workshop for which instructions are provided below:
There are three options for participating in this workshop for which instructions are provided below:

* via a [local install](#local-install)
* on [Google Colab](#google-colab)
* on [binder](#binder)

We recommend the [local install](#local-install) approach, especially if you forked
the repository, as it is the easiest way to keep a copy of your work and push back to GitHub.
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### Google Colab

Running on Colab is useful as it allows you to access GPU resources.
To run the notebooks in Google Colab click the following links for each of the exercises:

* [Exercise 01](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/exercises/01_penguin_classification.ipynb)
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* _If you leave a Colab session your work will be lost, so be careful to save any work
you want to keep._

### binder

If you cannot operate using a local install, and do not wish to sign up for a Colab account,
the exercises can be launched
[on binder](https://mybinder.org/v2/gh/Cambridge-ICCS/ml-training-material/main).

_Notes:_
* _It is not possible to save work from a binder session, so we advise using one of the above options.
This is an easy way to run the worked solutions, however._

## License

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