# Computing Environment for Class

## Our Course JupyterHub
[JupyterHub](https://jupyter.org/hub) is multi-user Jupyter environment designed for companies, classrooms and research labs.
This course will use a cloud-based JupyterHub environment supported by the [NSF LEAP STC](https://leap.columbia.edu/) and
managed by [2i2c](https://2i2c.org/service/):

[![Launch JupyterHub](https://img.shields.io/badge/jupyterhub-leap.2i2c.cloud-orange?style=for-the-badge&logo=jupyter)](https://leap.2i2c.cloud/)

A lot of documentation about this hub can be found at https://leap-stc.github.io/introduction/

You should already be familiar with this from CLMT 5045. 
If you need a refresher, please check out the material here: [here](https://earth-ds-ml.github.io/summer_2026/lectures_DS/computing_env/jupyterlab_and_colab.html).

## Computing environment and software libraries for machine learning
There are a number of different software libraries that can be used for machine learning. Python is the most popular programming language used 
for machine learning. Many of the most popular libraries for machine learning have been developed in Python, including `PyTorch`, `TensorFlow`, `JAX`, and `Keras`. Julia, a newer programming language focused on 
performance computing in scientific and technical fields also supports machine learning libraries. 

In this class, we will work with Python. We will use classic machine learning algorithms in the `sci-kit learn` library, as well as deep learning models implemented in `Tensorflow`.