This notebook explores Gaussian Processes to find theoretical functions and then uses advanced python machine learning libraries to explore sea level changes.
If you are unfamiliar with some of the concepts covered in this tutorial it's recommended to read through the background reading below either as you go through the notebook or beforehand.
If you want a quick look at the contents inside the notebook before deciding to run it please view the md file generated (note some HTML code not fully rendered)
Google CoLab
Google allows you 1 free GPU and this tutorial will run in less than an hour on googles sytem. Please save a copy in your google drive if you would like to save your work and model weights.
Binder
This notebook can run on Binder, for a quick look using saved model weights on the free CPU systems. The notebook will run in a few mintues but you will not be able to train your own model
Own Machine
If you're already familiar with git, anaconda and virtual environments the environment you need to create is found in GP.yml and the code below to install activate and launch the notebook. The GP.yml has been tested on the latest ubuntu, macOS and windows operating systems.
git clone [email protected]:cemac/LIFD_GaussianProcesses.git
cd GaussianProcesses
conda env create -f GP.yml
conda activate GP
jupyter-notebookThis notebook is designed to run on a laptop with no special hardware required therefore recommended to do a local installation as outlined in the repository howtorun and jupyter_notebooks sections.
LIFD_ENV_ML_NOTEBOOKS by cemac is licensed under a Creative Commons Attribution 4.0 International License.
Thanks to Oliver Pollard for the basis of this tutotial. This tutorial is part of the LIFD ENV ML NOTEBOOKS. Thanks to Donald Cummins and Tamora James for further contributions.