# Posts

### Assessing a Variational Autoencoder on MNIST using Pytorch

Learn how to visualize the latent space and generate data using a VAE in Pytorch.

### Minimalist Variational Autoencoder in Pytorch with CUDA GPU

Variational Autoencoders in Pytorch with CUDA GPU

### Variational Auto-Encoders and the Expectation-Maximization Algorithm

Relationship between Variational Autoencoders (VAE) and the Expectation Maximization Algorithm. Simple Explanation

### Towards SMC: Using the Dirac-delta function in Sampling and Sequential Monte Carlo

We derive the Dirac-delta function, explain how to use to approximate an Empirical PDF for a sample.

### Towards SMC: Sequential Importance Sampling

Sequential Importance Sampling intuition simply explained for SMC

### Towards SMC: Importance Sampling with Sequential Data

Importance Sampling tutorial for Sequential Data

### Towards SMC: Importance Sampling Explained

Importance Sampling as a first step towards Sequential Monte Carlo (SMC)

### Gaussian Expectation Propagation

Description of Expectation Propagation using Multivariate Normal factors for the global approximation.

### Multivariate Normal as an Exponential Family Distribution

How to rewrite a Multivariate Normal distribution as a member of the Exponential Family.

### Expectation Propagation - The Essential Minimum

The bare practical minimum to understand Expectation Propagation