## Introduction To Hamiltonian Monte Carlo

Introduction to Hamiltonian Monte Carlo One thing that has been occupying my head in the past couple of weeks has been HMC and how it can be used in large data/large model context. HMC stands for Hamiltonian Monte Carlo and it’s the de facto Bayesian method for sampling due to it’s speed. Before getting into big datasets and big models, let me motivate this problem a little bit. If you are new to Bayesian modelling, I have a little primer on the topic so I will assume for the most part you are familiar with basic Bayesianism. [Read More]

## Wasserstein variational autoencoders

Variational auto-encoders (VAEs) are a latent space model. The idea is you have some latent space variable $z \in \mathbb{R}^{k}$ which describes your original variables $x\in\mathbb{R}^d$ in higher dimensional space by a latent model $p(x|z)$. Let’s assume that this distribution is given by a neural network with some parameters $\theta$ so that we assume $$x | z, \theta \sim N(g_\theta(z), 1).$$ Of course in reality, we don’t know $(z, \theta)$, we would like to infer these from the data. [Read More]

## Introduction To Tensorflow Estimator

In this post I am going to introduce tf.estimator library. So first of all, what is this library trying to do? When writing tensorflow code, there is a lot of repeated operations that we need to do: read the data in batches process the data, e.g. convert images to floats run a for loop and take a few gradient descent steps save model weights to disk output metrics to tensorboard The keras library makes this quite a bit easier, but there are times when you might need to use plain old tensorflow (it gets quite hacky to implement some multiple output models and GANs in keras). [Read More]