Updated documentation.

Author: CodeReclaimers

docs/index.rst

@@ -1,28 +1,12 @@
 Welcome to NEAT-Python's documentation!
 =======================================
 
-**NOTE**: The examples are currently being moved into a separate repository (and
-Python package) from the NEAT library itself.  During this time if you use the
-code from the GitHub repository you may find that examples are missing and/or
-not working.
-
 NEAT (NeuroEvolution of Augmenting Topologies) is a method developed by Kenneth O. Stanley for evolving arbitrary neural
-networks. NEAT-Python is a Python implementation of NEAT.
-
-The core NEAT implementation is currently pure Python with no dependencies other than the Python standard library.
-The `visualize` module requires graphviz, NumPy, and matplotlib, but it is not necessary to install these packages unless
-you want to make use of these visualization utilities.  Some of the examples also make use of other libraries.
+networks. NEAT-Python is a pure Python implementation of NEAT, with no dependencies other than the Python standard library.
 
-If you need an easy performance boost, JIT-enabled `PyPy
-<http://pypy.org>`_ does a fantastic job, and may give you a ~10x speedup over CPython.  Once version 1.0 is out,
-Built-in C and OpenCL network implementations will be added to speed up applications for which network evaluation is the primary bottleneck.
-
-Support for HyperNEAT and other extensions to NEAT will also be added once the fundamental NEAT implementation is
+Support for HyperNEAT and other extensions to NEAT is planned once the fundamental NEAT implementation is
 more complete and stable.
 
-**Please note:** the package and its usage may change significantly while it is still in alpha status.  Updating to
-the most recent version is almost certainly going to break your code until the version number approaches 1.0.
-
 For further information regarding general concepts and theory, please see `Selected Publications
 <http://www.cs.ucf.edu/~kstanley/#publications>`_ on Stanley's website, or his recent `AMA on Reddit
 <https://www.reddit.com/r/IAmA/comments/3xqcrk/im_ken_stanley_artificial_intelligence_professor>`_.

docs/neat_overview.rst

@@ -6,8 +6,11 @@ detailed description of the algorithm, you should probably go read some of `Stan
 <http://www.cs.ucf.edu/~kstanley/#publications>`_ on his website.
 
 Even if you just want to get the gist of the algorithm, reading at least a couple of the early NEAT papers is a good
-idea.  Most of them are pretty short (8 pages or fewer), and do a good job of explaining concepts (or at least pointing
-you to other references that will).
+idea.  Most of them are pretty short, and do a good job of explaining concepts (or at least pointing
+you to other references that will).  `The initial NEAT paper
+<http://nn.cs.utexas.edu/downloads/papers/stanley.cec02.pdf>`_ is only 6 pages long, and Section II should be enough
+if you just want a high-level overview.
+
 
 In the current implementation of NEAT-Python, a population of individual genomes is maintained.  Each genome contains
 two sets of genes that describe how to build an artificial neural network: