Numerical Tours

of Data Sciences

Welcome to the Numerical Tours of Data Sciences

The Numerical Tours of Data Sciences, by Gabriel Peyré, gather Matlab, Python, Julia and R experiments to explore modern mathematical data sciences. They cover data sciences in a broad sense, 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 detailing the theory and the algorithms.

New Python Tours

Optimization by Laurent Condat

Updated tour

Entropic Regularization of Optimal Transport

Kernel Update

Jupyter notebooks

New tour

Geodesic Distance with Poisson Equation

Julia notebooks

using iJulia notebooks