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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 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.
Explore geometric interpretation of persistence with Applied Algebraic Topology Network. Learn about multi-scale representation, homotopy, and geodesic spaces in 1-2 hours.
Explore the magnitude of a metric space with the Applied Algebraic Topology Network. Understand its relation to persistent homology in less than an hour.
Explore topological data analysis with Applied Algebraic Topology Network. Learn about barcodes, point clouds, and limit theorems in less than an hour.
Explore the categorical perspective of persistence diagrams with the Applied Algebraic Topology Network. Learn about functors, bottleneck distance, and the algebraic stability of persistence barcodes.
Explore the Replicating Statistical Topology approach and learn to model persistence diagrams with the Applied Algebraic Topology Network. Less than 1-hour workload.
Explore topological and geometric approaches to studying molecular structures with the Applied Algebraic Topology Network. Less than 1-hour workload.
Explore algebraic varieties from samples with Applied Algebraic Topology Network. Learn about topology, algebraic geometry, and test algorithms on datasets using a Julia package.
Explore persistent homology and its applications in tracking noise during barcode computation with Applied Algebraic Topology Network. Learn approximation techniques and address open problems.
Explore topological data analysis with the Applied Algebraic Topology Network. Understand the Nerve Theorem, epsilon-acyclic covers, and refined interleaving in a 1-2 hour study.
Explore topological analysis of neural networks with Applied Algebraic Topology Network. Learn to extract and study large directed structural connectivity graphs. 1-2 hours workload.
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