Feature/var readme (#516)

* Add variational README

* Update variationalD README

torchbearer/variational/README.md

@@ -0,0 +1,34 @@
+# \[WIP\] torchbearer.variational
+A Variational Auto-Encoder library for PyTorch
+
+## Contents
+- [About](#about)
+- [Installation](#installation)
+- [Goals](#goals)
+
+<a name="about"/>
+
+## About
+
+Torchbearer.variational is a sub-package of [torchbearer](https://github.com/ecs-vlc/torchbearer) which is intended to
+re-implement state of the art models and practices relating to the world of Variational Auto-Encoders (VAEs). The goal
+is to provide everything from useful abstractions to complete re-implementations of papers. This is in order to support
+both research and teaching / learning regarding VAEs.
+
+<a name="installation"/>
+
+## Installation
+
+_variational_ comes packaged with torchbearer and so can be installed by following the instructions [here](https://github.com/ecs-vlc/torchbearer).
+
+<a name="goals"/>
+
+## Goals
+
+Currently, _variational_ only includes abstractions for simple VAEs and some accompaniments, the next steps are as follows:
+
+- Construct some separate part of the docs for the _variational_ content
+- Implement a series of standard models with associated notes pages and example usages
+- Implement other divergences not in PyTorch such as MMD, Jensen-Shannon, etc.
+- Implement and document tools for sampling the latent spaces of models and producing figures
+- Implement other dataloaders not in torchvision and add associated docs