VAE's are a mix between VI and Auto Encoders NN. They are used mainly for generating new data. In this video we will outline the theory behind the original paper, including looking at regular Auto Encoders, Variational Inference, and how they mix together to create VAE.

Original Paper (Kingma & Welling 2014): [ Ссылка ]

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“VI in R” Course Outline:
Administration
* Administration
Intro
* Intuition – what is VI?
* Notebook – Intuition
* Origin, Outline, Context
KL Divergence
* KL Introduction
* KL – Extra Intuition
* Notebook – KL – Exercises
* Notebook – KL – Additional Topics
* KL vs. Other Metrics
VI vs. ML
* VI (using KL) vs. Maximum Likelihood
ELBO & “Mean Field”
* ELBO
* “Mean Field” Approximation
Coordinate Ascent VI (CAVI)
* Coordinate Ascent VI (CAVI)
* Functional Derivative & Euler-Lagrange Equation
* CAVI – Toy Example
* CAVI – Bayesian GMM Example
* Notebook – Normal-Gamma Conjugate Prior
* Notebook – Bayesian GMM – Unknown Precision
* Notebook – Image Denoising (Ising Model)
Exponential Family
* CAVI for the Exponential Family
* Conjugacy in the Exponential Family
* Notebook – Latent Dirichlet Allocations Example
VI vs. EM
* VI vs. EM
Stochastic VI / Advanced VI
* SVI – Review
* SVI for Exponential Family
* Automatic Differentiation VI (ADVI)
* Notebook – ADVI Example (using STAN)
* Black Box VI (BBVI)
* Notebook – BBVI Example
Expectation Propagation
* Forward vs. Reverse KL
* Expectation Propagation
Variational Auto Encoder

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VI (restricted) playlist: [ Ссылка ]

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Intro/Outro Music: Dreamer - by Johny Grimes
[ Ссылка ]

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