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Mention XGBoost notebook
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donaldcummins authored Jun 6, 2024
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[![Twitter Follow](https://img.shields.io/twitter/follow/FluidsLeeds.svg?style=social&label=Follow)](https://twitter.com/FluidsLeeds)

Leeds Institute for Fluid Dynamics (LIFD) has teamed up with the Center for Environmental Modelling and Computation (CEMAC) team to create Jupyter notebook tutorials on the following topics.
Leeds Institute for Fluid Dynamics (LIFD) has teamed up with the Centre for Environmental Modelling and Computation (CEMAC) team to create Jupyter notebook tutorials on the following topics.

1. [ConvolutionalNeuralNetworks](/~https://github.com/cemac/LIFD_ConvolutionalNeuralNetworks)
2. [Physics_Informed_Neural_Networks](/~https://github.com/cemac/LIFD_Physics_Informed_Neural_Networks)
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5. [GenerativeAdversarialNetworks](/~https://github.com/cemac/LIFD_GenerativeAdversarialNetworks)
6. [AutoEncoders](/~https://github.com/cemac/LIFD_AutoEncoders)
7. [DimensionalityReduction](/~https://github.com/cemac/LIFD_AutoEncoders/LIFD_DimensionalityReduction)

8. [XGBoost](/~https://github.com/cemac/XGBoost-notebook)

**PLEASE NOTE YOU MUST CLONE RECURSIVELY (SEE BELOW)**

These notebooks require very little previous knowledge on a topic and will include links to further reading where necessary. Each Notebook should take about 2 hours to run through and should run out of the box home installations of Jupyter notebooks. These notebooks are designed with automatic checking of python environment files to remain easy to set up into the future.
These notebooks require very little previous knowledge on a topic and will include links to further reading where necessary. Each notebook will take about two hours to run through and should run out of the box on home installations of Jupyter notebooks. These notebooks are designed with automatic checking of Python environment files to remain easy to set up into the future.

As this resource grows in order to not make the repository unwieldy this repository is made up of submodules this means you can clone
As this resource grows, in order to not make the repository unwieldy this repository is made up of submodules that can be cloned individually.

## How do I get started ??
## How do I get started?

Some tutorials are so lightweight you can run them on [binder](https://mybinder.readthedocs.io/en/latest/#what-is-binder) the others we recommend running on your local machine to get started either clone this repository (**LARGE SIZE**) or select a tutorial to clone and run each tutorial separately.
Some tutorials are so lightweight you can run them on [binder](https://mybinder.readthedocs.io/en/latest/#what-is-binder). The others we recommend running on your local machine. To get started, either clone this repository (**LARGE SIZE**) or select a tutorial to clone and run each tutorial separately.

### Colab enabled tutorials

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git clone --recursive git@github.com:cemac/LIFD_ENV_ML_NOTEBOOKS.git
``

then follow the Individual README.md instructions
then follow the individual README.md instructions.

### Cloning individual tutorials

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5. `git clone --recursive git@github.com:cemac/LIFD_GenerativeAdversarialNetworks.git`
6. `git clone --recursive git@github.com:cemac/LIFD_AutoEncoders.git`
7. `git clone --recursive git@github.com:cemac/LIFD_DimensionalityReduction.git`
8. `git clone --recursive git@github.com:cemac/XGBoost-notebook.git`

## How to Run

These notebooks can run with the resources provided and the anaconda environment setup. If you are familiar with anaconda, jupyter notebooks and GitHub. Simply clone this repository and run it within your Jupyter Notebook setup. Otherwise please read the [how to run](howtorun.md) guide. Induvidual notebooks have bespoke instructions
These notebooks can run with the resources provided and the Anaconda environment setup. If you are familiar with Anaconda, Jupyter notebooks and GitHub then simply clone this repository and run it within your Jupyter notebook setup. Otherwise, please read the [how to run](howtorun.md) guide. Individual notebooks have bespoke instructions.


```bash
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**Python**

It is recommended you use [anaconda](https://medium.com/pankajmathur/what-is-anaconda-and-why-should-i-bother-about-it-4744915bf3e6) to manage the python packages required. Sore Machine learning libraries are large and if you only wish to run one notebook consider installing the environment provided for that specific notebook. Otherwise, you can install all required packages running the following commands.
It is recommended you use [Anaconda](https://medium.com/pankajmathur/what-is-anaconda-and-why-should-i-bother-about-it-4744915bf3e6) to manage the Python packages required. Some machine-learning libraries are large and if you only wish to run one notebook consider installing the environment provided for that specific notebook. Otherwise, you can install all required packages running the following commands.

```bash
conda env create -f <env-file>.yml
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**What if I forgot to clone recursively?**

Not to worry in your cloned folder simply run:
Not to worry. In your cloned folder simply run:

```bash
git submodule init
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**Hardware**

These notebooks are designed to run on a personal computer. Although please note the techniques demonstrated can be very computationally intensive so there may be options to skip steps depending on the hardware available. e.g. use pre-trained models.
These notebooks are designed to run on a personal computer. Although please note the techniques demonstrated can be very computationally intensive, so there may be options to skip steps depending on the hardware available, e.g. use pre-trained models.

**Knowledge**

No background knowledge is required of the environmental Science concepts or machine learning concepts. We have assumed some foundational knowledge but links are provided to in-depth information on the fundamentals of each concept
No background knowledge of the environmental-science or machine-learning concepts is required. We have assumed some foundational knowledge but links are provided to in-depth information on the fundamentals of each concept.

## Contributions

We hope that this resource can be built upon to provide a wealth of training material for Earth Science Machine Learning topics at Leeds
We hope that this resource can be built upon to provide a wealth of training material for Earth-science machine-learning topics at Leeds.

# Licence information #

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" property="dct:title">LIFD_ENV_ML_NOTEBOOKS</span> by <a xmlns:cc="http://creativecommons.org/ns#" href="http://cemac.leeds.ac.uk/" property="cc:attributionName" rel="cc:attributionURL">cemac</a> is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.

## Acknowledgements

*Leeds Institute of Fluid Dynamics*, *CEMAC*, *Helen Burns*, *Matthew Gaddes*, *Oliver Pollard*, *Chetan Deva*, *Fergus Shone*, *Michael MacRaild*, *Phil Livermore*, *Giulia Fedrizzi*, *Eszter Kovacs*, *Ana Reyna Flores*, *Francesca Morris*, *Emma Pearce*, *Maeve Murphy Quinlan*, *Sara Osman*, *Jonathan Coney*, *Eilish O'grady*, *Leif Denby*, *Sandra Piazolo*, *Caitlin Howath*, *Claire Bartholomew*, *Anna Hogg*, *Ali Gooya* and *Donald Cummins* and *Tamora James*.
*Leeds Institute of Fluid Dynamics*, *CEMAC*, *Helen Burns*, *Matthew Gaddes*, *Oliver Pollard*, *Chetan Deva*, *Fergus Shone*, *Michael MacRaild*, *Phil Livermore*, *Giulia Fedrizzi*, *Eszter Kovacs*, *Ana Reyna Flores*, *Francesca Morris*, *Emma Pearce*, *Maeve Murphy Quinlan*, *Sara Osman*, *Jonathan Coney*, *Eilish O'grady*, *Leif Denby*, *Sandra Piazolo*, *Caitlin Howarth*, *Claire Bartholomew*, *Anna Hogg*, *Ali Gooya*, *Tamora James* and *Donald Cummins*.

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