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(Fourier for fun and practice nD)
Текущая версия (21:29, 6 сентября 2021) (править) (отменить)
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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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==Theme 1: PDE==
 
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(after RBF)
 
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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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Fundamental theorems

W: Inverse function theorem and Jacobian

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