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- | * Geometric deep learning
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- | * Functional data analysis
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- | * Applied mathematics for machine learning
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- | ==General principles==
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- | 1. The experiment and measurements defines axioms i
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- | ==Syllabus and goals==
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- | ==Theme 1: ==
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- | ===Message===
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- | ===Basics===
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- | ===Application===
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- | ===Code===
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- | https://papers.nips.cc/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf
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- | ==Theme 1: Manifolds ==
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- | ==Code==
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- | Surface differential geometry
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- | [https://www.coursera.org/lecture/image-processing/3-surface-differential-geometry-duration-11-43-vtuJ1 Coursera code video] for Image and Video Processing
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- | ==Theme 1: ODE and flows==
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- | *[https://papers.nips.cc/paper/2018/hash/69386f6bb1dfed68692a24c8686939b9-Abstract.html Neural Ordinary Differential Equations] (source paper and code)
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- | *[https://en.wikipedia.org/wiki/Flow-based_generative_model W: Flow-based generative model]
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- | *[https://deepgenerativemodels.github.io/notes/flow/ Flows at deepgenerativemodels.github.io]
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- | *[https://habr.com/ru/company/ods/blog/442002/ Знакомство с Neural ODE на хабре]
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- | Goes to BME
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- | *[https://arxiv.org/pdf/1505.05770.pdf Variational Inference with Normalizing Flows (source paper)]
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- | *[https://lilianweng.github.io/lil-log/2018/10/13/flow-based-deep-generative-models.html Flow-based deep generative models]
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- | (after RBF)
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- | ==Theme 1: PDE==
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- | ==Theme 1: Navier-Stokes equations and viscous flow==
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- | == Fourier for fun and practice 1D==
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- | [https://morioh.com/p/18b3158eab36?f=5c21fb01c16e2556b555ab32&fbclid=IwAR0FBF6IfmEaaedMEDzxiSclxGgNVweHgDQZympUZ-4doeKGNwQwUvp9upo Fourier Code]
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- | == Fourier for fun and practice nD==
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- | See:
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- | * Fourier analysis on Manifolds 5G page 49
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- | * Spectral analysis on meshes
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- | == Geometric Algebra ==
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- | experior product and quaternions
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- | ==Theme 1: High order splines==
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- | ==Theme 1: Topological data analysis==
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- | ==Theme 1: Homology versus homotopy==
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- | [https://en.wikipedia.org/wiki/Homology_(mathematics) W: Homology]
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