# NumPy tutorials

## Contents

# NumPy tutorials#

This set of tutorials and educational materials is being developed in the numpy-tutorials repository, and is not a part of the NumPy source tree. The goal of this repository is to provide high-quality resources by the NumPy project, both for self-learning and for teaching classes with. If you’re interested in adding your own content, check the Contributing section.

To open a live version of the content, click the **launch Binder** button above.
To open each of the `.md`

files, right click and select “Open with -> Notebook”.
You can also launch individual tutorials on Binder by clicking on the rocket
icon that appears in the upper-right corner of each tutorial. To download a
local copy of the `.ipynb`

files, you can either
clone this repository
or use the download icon in the upper-right corner of each tutorial.

## Content#

- NumPy Features
- NumPy Applications
- Determining Moore’s Law with real data in NumPy
- Deep learning on MNIST
- Deep reinforcement learning with Pong from pixels
- Sentiment Analysis on notable speeches of the last decade
- X-ray image processing
- Determining Static Equilibrium in NumPy
- Plotting Fractals
- Analyzing the impact of the lockdown on air quality in Delhi, India

- Contributing

## Useful links and resources#

The following links may be useful:

Note that regular documentation issues for NumPy can be found in the main NumPy
repository (see the `Documentation`

labels there).