https://github.com/cemac/lifd_dimensionalityreduction
Jupyter Notebooks Dimensionality Reduction
Science Score: 23.0%
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Repository
Jupyter Notebooks Dimensionality Reduction
Basic Info
- Host: GitHub
- Owner: cemac
- License: cc-by-4.0
- Language: Jupyter Notebook
- Default Branch: main
- Homepage: https://cemac.github.io/LIFD_ENV_ML_NOTEBOOKS/
- Size: 2.91 MB
Statistics
- Stars: 1
- Watchers: 0
- Forks: 1
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Leeds Institute for Fluid Dynamics Machine Learning For Earth Sciences
Dimensionality Reduction
This Jupyter notebook is based on Jonathan Coney's work on identifying and characterising trapped lee waves using dimensionality reduction. The work is outlined in Coney et al., 2023. This notebook will go through the basics of Principal Component Analysis and Dimensionality Reduction methods using some toy code from a Kaggle tutorial and the MNIST dataset, and then apply those methods to an Earth Science application based on Jonathan Coney's work.
Quick look
Quick start
Binder and Colab buttons
Will launch this tutorial in binder (CPU) or Google Colab (GPU)
Running Locally
If you're already familiar with git, Anaconda and virtual environments, the environment you need to create is found in DR.yml and the code below will install, activate and launch the notebook. The .yml file has been tested on the latest Linux, macOS and Windows operating systems.
bash
git clone git@github.com:cemac/LIFD_DimensionalityReduction.git
cd LIFD_DimensionalityReduction
conda env create -f DR.yml
conda activate DR
jupyter-notebook
Installation and Requirements
This notebook is designed to run on a laptop with no special hardware required. Therefore, it is recommended to do a local installation as outlined in the repository howtorun and jupyter_notebooks sections.
Licence information
LIFDENVML_NOTEBOOKS by cemac is licensed under a Creative Commons Attribution 4.0 International License.
Acknowledgements
Thanks to Jonathan Coney for the basis of this tutorial. This tutorial is part of the LIFD ENV ML NOTEBOOKS series. Please refer to the parent repository for full acknowledgements.