8 skills found
lululxvi / DeeponetLearning nonlinear operators via DeepONet based on the universal approximation theorem of operators
jbramburger / DataDrivenDynSystScripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems
zixin2006 / Paper Interpretation Handout The Universal Approximation Of Neural NetworksThis repository includes a handout that attempts to outline the entire proof process of the paper written by Cybenko 1989 about the universal approximation theorem of neural networks. The handout clarifies some points of confusion I encountered during my reading, and provides deeper mathematical explanations for the details Cybenko skipped over.
Isaac-Somerville / Neural Networks For Solving Differential EquationsCodebase for Master's dissertation in Mathematics at Durham University. Topic: applying neural networks to differential equations. Grade: 85/100.
timothylimyl / UATExplanation of Universal Approximation Theorem with Code
Crazz-Zaac / Computer Vision ExperimentsThis project delves into the applications of neural networks in image processing and computer vision, driven by our curiosity about their potential beyond theoretical foundations like the Universal Approximation Theorem.
Tanwar-12 / Universal Approximation TheoremThe Universal Approximation Theorem implies that neural networks can approximate any continuous function given enough neurons and layers.
tongwang01 / Uat ExperimentExperiments with the Universal Approximation Theorem for Neural Networks
