In this tutorial, we will use a [matrix decomposition](https://en.wikipedia.org/wiki/Matrix_decomposition) from linear algebra, the Singular Value Decomposition, to generate a compressed approximation of an image. We'll use the `face` image from the [scipy.datasets](https://docs.scipy.org/doc/scipy/reference/datasets.html) module:

**Note**: If you prefer, you can use your own image as you work through this tutorial. In order to transform your image into a NumPy array that can be manipulated, you can use the `imread` function from the [matplotlib.pyplot](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.html#module-matplotlib.pyplot) submodule. Alternatively, you can use the [imageio.imread](https://imageio.readthedocs.io/en/stable/userapi.html#imageio.imread) function from the `imageio` library. Be aware that if you use your own image, you'll likely need to adapt the steps below. For more information on how images are treated when converted to NumPy arrays, see [A crash course on NumPy for images](https://scikit-image.org/docs/stable/user_guide/numpy_images.html) from the `scikit-image` documentation.