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Introduction to Geometric Learning in Python with Geomstats

Nina Miolane
Stanford University

Nicolas Guigui
Université Côte d'Azur, Inria

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



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: and


differential geometry, statistics, manifold, machine learning



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