Introduction to Geometric Learning in Python with Geomstats
Hadi Zaatiti
Christian Shewmake
Hatem Hajri
Daniel Brooks
Alice Le Brigant
Johan Mathe
Benjamin Hou
Yann Thanwerdas
Stefan Heyder
Olivier Peltre
Niklas Koep
Yann Cabanes
Thomas Gerald
Paul Chauchat
Bernhard Kainz
Claire Donnat
Susan Holmes
Xavier Pennec
Video: https://youtu.be/Ju-Wsd84uG0
Abstract
There is a growing interest in leveraging differential geometry in the machine learning community. Yet, the adoption of the associated geometric computations has been inhibited by the lack of a reference implementation. Such an implementation should typically allow its users: (i) to get intuition on concepts from differential geometry through a hands-on approach, often not provided by traditional textbooks; and (ii) to run geometric machine learning algorithms seamlessly, without delving into the mathematical details. To address this gap, we present the open-source Python package geomstats and introduce hands-on tutorials for differential geometry and geometric machine learning algorithms - Geometric Learning - that rely on it. Code and documentation: github.com/geomstats/geomstats and geomstats.ai.
differential geometry, statistics, manifold, machine learning
DOI10.25080/Majora-342d178e-007