The Numerical Tours

The Numerical Tours of Data Sciences, by Gabriel Peyré, gather Matlab, Python and Julia experiments to explore modern data science. They cover data science in a broad since, including imaging, machine learning, computer vision and computer graphics. It showcases application of numerical and mathematical methods such as convex optimization, PDEs, optimal transport, inverse problems, sparsity, etc. The tours are complemented by slides of courses that concentrate on the theory of signal and image processing.

How to use these tours?

Each tour is a set of experiments that can be performed using either Matlab, Python or Julia. At the beginning of each tour, you are asked to download and install the required toolboxes, that contain many useful helper functions. Each tour alternates between code you can copy/paste and exercises you need to solve on your own.

How to cite the Numerical Tours

If you are using codes from these Numerical Tours for your own research, you should cite the Numerical Tours as:

G. Peyré, The Numerical Tours of Signal Processing - Advanced Computational Signal and Image Processing IEEE Computing in Science and Engineering, vol. 13(4), pp. 94-97, 2011.

How to make your own tours?

For the Matlab tours, each Numerical Tour is a HTML web page automatically generated using a Matlab .m script, that both executes the instructions of the tour, and generates the web page. These .m files are then also translated into a Jupyter notebook.

You can download the Numerical Tour Publishing Toolbox, and use the same tools to create your own Numerical Tours. This can be very useful to generate numerical exercises for your students, or to present your research in a more attracting way.

The Python and Julia tours are Jupyter notebooks.

Mathematical Tours

The [Mathematical Tours of Data Sciences] is a companion website which presents the mathematical concepts underlying the Numerical Tours.


If you have any question regarding the Numerical Tours, feel free to contact me.