Minor cleanup to quickstart guide.

Author: rdadolf

docs/quickstart.md

@@ -1,11 +1,10 @@
 # Installing Prerequisites
 
-Fathom requires a couple of core packages:
-
- - Python 2.6+
+Fathom requires a fair number of other software packages to use. TensorFlow is the obvious dependency, but there are a number of other support libraries which are mostly used for data processing and ingest. Deep learning algorithms operate on real data, so many of them have to do a substantial amount of work to turn raw inputs into a form they can process efficiently.
 
 ## TensorFlow
 
+ - Python 2.6+
  - [TensorFlow 0.8.0rc0](https://github.com/tensorflow/tensorflow/releases/tag/v0.8.0rc0)
 
 For TensorFlow, you can either download a pre-built binary or build from source. The latter is more involved, but can allow more flexibility in configuration (i.e.- you can pass specific options to the underlying math libraries which can affect performance). To build from source, you'll also need Bazel, Google's build system. Instructions can be found in the TensorFlow documentation (archived here):
@@ -44,22 +43,23 @@ The [Arcade Learning Environment (ALE)](http://www.arcadelearningenvironment.org
 
 [OpenCV](http://opencv.org/) is a collection of image processing and computational geometry functions designed to support computer vision. You'll need both a 2.x version of the backend library and also the python interface wrapper. Many Linux distributions have a package for both (Ubuntu's are `libopencv-dev` and `python-opencv`), but you can also [build from source ](http://docs.opencv.org/2.4.13/doc/tutorials/introduction/linux_install/linux_install.html) and then use `pip` to install the `opencv-python` wrapper.
 
-# Downloading Data
-
-*In progress*
-
-# Running the Workloads
-
-*In progress*
-
 # Alternative: Quickstart via Docker
 
-If you don't need accurate performance numbers right away, we also provide a pre-built Docker image to make it easy to get familiar with the Fathom workloads.
+If you don't need accurate performance numbers right away, we also provide a pre-built [Docker image](https://hub.docker.com/r/rdadolf/fathom/) to make it easy to get familiar with the Fathom workloads.
 
 If you're not familiar with Docker, you can think of it as a lightweight virtualization layer, similar to a VM but at a higher level of abstraction. Installation instructions can be found on the [docker website](https://www.docker.com/). To run the Fathom image interactively, use this:
 
 ```sh
 docker run -it rdadolf/fathom
 ```
 
-The image will automatically be downloaded from the [docker hub](https://hub.docker.com/r/rdadolf/fathom/), launched, and you'll be given a shell prompt with the environment all set up.
+The image will automatically be downloaded from the Docker hub, launched, and you'll be given a shell prompt with the environment all set up.
+
+# Downloading Data
+
+*In progress*
+
+# Running the Workloads
+
+*In progress*
+