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Introduction to Geometric Learning in Python with Geomstats

Nina Miolane
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Hadi Zaatiti
Hatem Hajri
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Yann Thanwerdas
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Paul Chauchat
Susan Holmes
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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
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Dates and versions

hal-02908006 , version 1 (28-07-2020)



Nina Miolane, Nicolas Guigui, Hadi Zaatiti, Christian Shewmake, Hatem Hajri, et al.. Introduction to Geometric Learning in Python with Geomstats. SciPy 2020 - 19th Python in Science Conference, Jul 2020, Austin, Texas, United States. pp.48-57, ⟨10.25080/Majora-342d178e-007⟩. ⟨hal-02908006⟩
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