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Learn how quantum computers can identify patterns in data, reducing execution time and energy consumption, in this tutorial by the Applied Algebraic Topology Network.
Explore new and old invariants for tame parametrised chain complexes with Applied Algebraic Topology Network. Learn about explicit constructions, comprehensiveness, and discriminativeness in 1-2 hours.
Explore the application of (co)localisation techniques from homotopy theory in data analysis with this less than 1-hour seminar by the Applied Algebraic Topology Network.
Explore topological data analysis with a focus on lower dimensional features in this under 1-hour offering from the Applied Algebraic Topology Network. Learn detection methods and applications in image segmentation.
Explore the discriminative power of the fibered bar code in multiparameter persistence modules with Applied Algebraic Topology Network. Less than 1-hour workload.
Explore the application of topological data analysis in chemistry with Aurora Clark from Washington State University. Learn to extract new information from chemical data in a 1-2 hour webinar.
Explore catastrophe theory with Applied Algebraic Topology Network. Learn about critical points, the algebra of germs, and universal unfoldings. Includes practical chemistry application.
Explore the applications of TDA software in data science with Python in this interactive session by the Applied Algebraic Topology Network. Less than 1-hour workload.
Explore Morse theory with the Applied Algebraic Topology Network. Learn about CW cell complexes, Morse lemma, Morse-Smale complexes, and more in under an hour.
Explore the use of cyberinfrastructure in science gateways, with a focus on molecular sciences, in this less than 1-hour talk by the Applied Algebraic Topology Network.
Explore topological data analysis with the Applied Algebraic Topology Network. Learn to use Mapper, a data visualization technique, to reveal hidden structures in complex data sets in under an hour.
Explore the featurization of persistence diagrams using template functions for machine learning tasks with Applied Algebraic Topology Network. Less than 1-hour workload.
Explore the formation of giant cycles in random structures with the Applied Algebraic Topology Network. Learn about persistent homology, signal vs noise differentiation, and Euler characteristics in 1-2 hours.
Explore the Wasserstein distance between persistence diagrams with Applied Algebraic Topology Network. Understand why q=p in the p-Wasserstein distance, its implications, and benefits.
Explore the parameterized complexity of quantum invariants of knots with the Applied Algebraic Topology Network. Learn about Reshetikhin-Turaev invariants and related mathematical conjectures.
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