These tutorials are written as Jupyter Notebooks
If you want to take a quick look PDFs or markdowns have been generated for each notebook requiring no software requirements
To run on your machine first clone this repo via your preferred method e.g. cloning via terminal or cloning via gitbash (windows)
Some notebooks require additional code from other git repos
In your terminal (Mac or Linux) or your gitbash terminal run
git clone --recursive git@github.com:cemac/LIFD_ConvolutionalNeuralNetworks.gitor if you have already cloned but forgotten to get the submodules:
cd LIFD_ENV_ML_NOTEBOOKS
git submodule update --init --recursivegit clone --recursive git@github.com:cemac/LIFD_ConvolutionalNeuralNetworks.gitgit clone --recursive git@github.com:cemac/LIFD_RandomForests.gitgit clone --recursive git@github.com:cemac/LIFD_GaussianProcesses.gitgit clone --recursive git@github.com:cemac/LIFD_Physics_Informed_Neural_Networks.git
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.
Python
It is recommended you use anaconda 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.
conda env create -f <env-file>.yml
conda activate <env-name>
# save yourself some space with one extra command
conda clean -a
jupyter-notebook # launches the notebook serverfurther information can be found in the jupyter_notebooks guide. For set up and Troubleshooting tips.
Notebooks that are binder compatible have a binder launch button in their readme.