Updated README

Author: cemoody

README.rst

@@ -16,10 +16,70 @@ lda2vec: Tools for interpreting natural language
 .. image:: https://img.shields.io/twitter/follow/chrisemoody.svg?style=social
     :target: https://twitter.com/intent/follow?screen_name=chrisemoody
 
+The lda2vec model tries to mix the best parts of word2vec and LDA
+into a single framework. word2vec captures powerful relationships 
+between words, but the resulting vectors are largely interpretable
+and don't represent documents. LDA on the other hand is quite
+interpretable by humans, but doesn't model local word relationships
+like word2vec. We build a model that builds both word and document
+topics, makes them interpreable,  makes topics over clients, times,
+and documents, and makes them supervised topics.
+
+
+Resources
+---------
+See this `Jupyter Notebook <http://nbviewer.jupyter.org/github/cemoody/lda2vec/blob/master/examples/twenty_newsgroups/lda.ipynb>`_
+for an example of an end-to-end demonstration.
+
+See this `presentation <http://www.slideshare.net/ChristopherMoody3/word2vec-lda-and-introducing-a-new-hybrid-algorithm-lda2vec-57135994>`_
+for a presentation focused on the benefits of word2vec, LDA, and lda2vec.
+
+See the `API reference docs <https://lda2vec.readthedocs.org/en/latest/>`_
+
+
+About
+-----
+
+.. image:: images/img00_word2vec.png
+
+Word2vec tries to model word-to-word relationships.
+
+.. image:: images/img01_lda.png
+
+LDA models document-to-word relationships.
+
+.. image:: images/img02_lda_topics.png
+
+LDA yields topics over each document.
+
+.. image:: images/img03_lda2vec_topics01.png
+
+lda2vec yields topics not over just documents, but also regions.
+
+.. image:: images/img04_lda2vec_topics02.png
+
+lda2vec also yields topics over clients.
+
+.. image:: images/img05_lda2vec_topics03_supervised.png
+
+lda2vec the topics can be 'supervised' and forced to predict another target.
+
+lda2vec also includes more contexts and features than LDA. LDA dictates that
+words are generated by a document vector; but we might have all kinds of
+'side-information' that should influence our topics. For example, a single
+client comment is about a particular item ID, written at a particular time
+and in a particular region. In this case, lda2vec gives you topics over all
+items (separating jeans from shirts, for example) times (winter versus summer)
+regions (desert versus coastal) and clients (sporty vs professional attire).
+
+Ultimately, the topics are interpreted using the excellent pyLDAvis library:
+
+.. image:: images/img06_pyldavis.gif
+
+
 Requirements
 ------------
 
-
 Minimum requirements:
 
 - Python 2.7+

TODO

@@ -1,4 +1,5 @@
 Add examples of specific documents to 20ng example
+Keep track of doc counts between model serializations
 Add bigramming
 Add better README
 Add an example script with HN with doc id, client id, and predicted score