Участник:Strijov/Drafts

Материал из MachineLearning.

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(Fourier for fun and practice nD)
(Theme 1: PDE)
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*[https://arxiv.org/pdf/1505.05770.pdf Variational Inference with Normalizing Flows (source paper)]
*[https://arxiv.org/pdf/1505.05770.pdf Variational Inference with Normalizing Flows (source paper)]
*[https://lilianweng.github.io/lil-log/2018/10/13/flow-based-deep-generative-models.html Flow-based deep generative models]
*[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==
==Theme 1: PDE==
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==Theme 1: Navier-Stokes equations and viscous flow==
== Fourier for fun and practice 1D==
== Fourier for fun and practice 1D==

Версия 20:11, 1 августа 2021

Содержание

  • Geometric deep learning
  • Functional data analysis
  • Applied mathematics for machine learning


General principles

1. The experiment and measurements defines axioms i


Syllabus and goals

Theme 1:

Message

Basics

Application

Code

https://papers.nips.cc/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf


Theme 1: Manifolds

Code

Surface differential geometry Coursera code video for Image and Video Processing

Theme 1: ODE and flows

Goes to BME

(after RBF)

Theme 1: PDE

Theme 1: Navier-Stokes equations and viscous flow

Fourier for fun and practice 1D

Fourier Code



Fourier for fun and practice nD

See:

  • Fourier analysis on Manifolds 5G page 49
  • Spectral analysis on meshes

Geometric Algebra

experior product and quaternions


Theme 1: High order splines

Theme 1: Topological data analysis

Theme 1: Homology versus homotopy

W: Homology



Fundamental theorems

W: Inverse function theorem and Jacobian

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