This talk will provide a brief overview of Optimal Transport (OT) and its uses in the development of Machine Learning applications, with the aim of encouraging the adoption of the OT toolbox by those using AI/ML tools in their scientific research and demonstrations of the main OT algorithms in the form of Jupyter Notebooks.

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