Made some changes to documentation.

docs/source/index.rst

@@ -6,10 +6,11 @@ Yet Another Neural Network Toolbox
 ==================================    
 
 Welcome to the Yann Toolbox. It is a toolbox for building and learning convolutional neural 
-networks, built on top of `theano`_. To setup the toolbox refer the :ref:`setup` guide. Once setup, 
-you may start with the :ref:`quick_start` guide or try your hand at the :ref:`tutorial` and the 
-guide to :ref:`getting_started`. A user base discussion group is setup on `gitter`_ and also on 
-`google groups`_.
+networks, built on top of `theano`_. This toolbox is a homage to Prof. 
+`Yann LeCun <http://yann.lecun.com/>`_, one of the earliest poineers of CNNs. To setup the toolbox 
+refer the :ref:`setup` guide. Once setup, you may start with the :ref:`quick_start` guide or try 
+your hand at the :ref:`tutorial` and the guide to :ref:`getting_started`. A user base discussion 
+group is setup on `gitter`_ and also on `google groups`_.
 
 
 .. image:: https://travis-ci.org/ragavvenkatesan/yann.svg?branch=master
@@ -50,22 +51,24 @@ guide to :ref:`getting_started`. A user base discussion group is setup on `gitte
 
     Yann is currently under its early phases and is presently undergoing massive development. 
     Expect a lot of changes. Unittests are only starting to be written, therefore the 
-    coverage and travis builds are not to be completely trusted. The toolbox will be 
+    coverage and travis build passes are not to be completely trusted. The toolbox will be 
     formalized in the future but at this moment, the authorship, coverage and maintanence of the 
     toolbox is under extremely limited manpower.
 
 .. note ::
     
     While, there are more formal and wholesome toolboxes that are similar and have a much larger 
     userbase such as `Lasagne`_, `Keras`_, `Blocks`_ and `Caffe`_, this toolbox is designed 
-    differently, simpler and versatile. Yann is designed as a supplement to an upcoming book on 
-    Convolutional Neural Networks and also the toolbox of choice for a deep learning course. 
+    differently. This is much simpler and versatile. Yann is designed as a supplement to an 
+    upcoming beginner's book on Convolutional Neural Networks and also the toolbox of choice for a 
+    introductory course on deep learning for computer vision. 
     
     Because of this reason, Yann is specifically designed to be intuitive and easy to use for 
     beginners. That does not compromise Yann of any of its core purpose - to be able to build CNNs 
     in a plug and play fashion. It is still a good choice for a toolbox for running pre-trained 
     models and build complicated, non-vannilla CNN architectures that are not easy to build with 
-    the other toolboxes.
+    the other toolboxes. It is also a good choice for researchers and industrial scientists, who 
+    want to quickly prototype networks and test them before developing production scale models.
 
 .. _getting_started: 
 

docs/source/pantry/tutorials/lenet.rst

@@ -10,7 +10,8 @@ By virture of being here, it is assumed that you have gone through the :ref:`qui
     Code is done, but text needs to be written in. 
 
 The full code for this tutorial with additional commentary can be found in the file 
-``pantry.tutorials.lenet.py``.
+``pantry.tutorials.lenet.py``. If you have toolbox cloned or downloaded or just the tutorials 
+downloaded, Run the code as,
 
 .. automodule:: pantry.tutorials.lenet
    :members:

docs/source/pantry/tutorials/mlp.rst

@@ -112,12 +112,12 @@ term 'era' to represent each set of epochs running with one learning rate.
 annealing parameter. ``learning_rates`` is naturally of length that is one higher than ``epochs``.
 ``show_progress`` will print a progress bar for each epoch. ``validate_after_epochs`` will perform 
 validation after such many epochs on a different validation dataset. The full code for this tutorial
-with additional commentary can be found in the file ``pantry.tutorials.mlp.py``. Run the code as 
-following:
+with additional commentary can be found in the file ``pantry.tutorials.mlp.py``. If you have 
+toolbox cloned or downloaded or just the tutorials downloaded, Run the code as,
 
 .. code-block:: python
 
-    from pantry.examples.mlp import mlp
+    from pantry.tutorials.mlp import mlp
     mlp(dataset = 'some dataset created')
 
 or simply,

docs/source/setup.rst

@@ -226,12 +226,23 @@ Dependencies for visualization
 
     pip insall matplotlib
   
-cPickle and gzip
-----------------
+cPickle, gzip and hdf5py 
+------------------------
 
-  Most often the case is that these come with the python installation, 
+  Most often the case is that `cPickle` and `gzip` these come with the python installation, 
   if not please install them.  Yann uses these for saving down models and such.
 
+  For datasets, at the moment, yann uses cpickle. In the future, yann will migrate to hdf5 for 
+  datasets. Install hdf5py by running either,
+
+  .. code-block:: bash
+    conda install h5py
+
+  or, 
+
+  .. code-block:: bash
+    pip install h5py
+
 
 Yann Toolbox Setup
 ====================

docs/source/trailer.rst

@@ -3,14 +3,14 @@
 The story behind Yann 
 =====================
 
-I am `Ragav Venkatesan`_, the author of Yann. I started doing convolutional neural networks
-in the begining of 2015. I am not that big in c++, so I started looking at `theano`_ and started 
+I am `Ragav Venkatesan`_, the creator of Yann. I started building convolutional neural networks
+in early 2015. I began with `theano`_ and started 
 following and implementing their tutorials. As I started reading new papers and coding new 
 technologies, I slowly integrated them into what was soon developing into a toolbox. My lab mates at
 `Visual Representaiton and Processing Group`_ also started getting into CNN research and started 
 using my toolbox so I formalized it and hosted it on `GitHub`_. Originally it was a completely 
-unstructured and completely demodularized toolbox and went with the name samosa. The original 
-codebase still exists in older commits on the git. This toolbox still at its core is the 
+unstructured and completely demodularized toolbox and went with the name 'samosa'. The original 
+codebase still exists in older commits on the git. This toolbox still at its core is still the 
 theano tutorials from which it was built.
 
 After considerable effort being put in to make this toolbox modular, and testing it out and after 
@@ -19,8 +19,8 @@ something that I am now proud to share with the rest of DL community with.
 
 This toolbox is also slated to be used with the course CSE 591: Introduction to Deep Learning for 
 Computer Vision at ASU in Spring of 2017. With more features being added into the toolbox, I figured
-I would clean it up, formalize it and write some good documentation so that interested people could use 
-it after. Thus after being rechristened as Yann, this toolbox was born.
+I would clean it up, formalize it and write some good documentation so that interested people could 
+use it after. Thus after being rechristened as Yann, this toolbox was born.
 
 .. warning ::
     
@@ -40,20 +40,6 @@ it after. Thus after being rechristened as Yann, this toolbox was born.
     backend I used for this toolbox. As the course begins, I will add more tutorials with notes that
     will make this more useful and interactive.
 
-.. _theano: http://deeplearning.net/software/theano/ 
-.. _GitHub: https://github.com/ragavvenkatesan/yann
-.. _Ragav Venkatesan: http://www.public.asu.edu/~rvenka10/
-.. _Visual Representaiton and Processing Group: http://www.public.asu.edu/~bli24/Research.html
-.. _Lasagne: https://github.com/Lasagne/Lasagne
-.. _Keras: http://keras.io/
-.. _Caffe: http://caffe.berkeleyvision.org/
-.. _Blocks: https://blocks.readthedocs.io/en/latest/
-.. _theano tutorials: http://deeplearning.net/software/theano/tutorial/examples.html 
-.. _skdata's: https://jaberg.github.io/skdata/
-.. _Fuel: https://github.com/mila-udem/fuel
-.. _Sebastien Bubeck's: https://blogs.princeton.edu/imabandit/2013/04/01/acceleratedgradientdescent/
-
-
 What is in the toolbox ? 
 ========================
 
@@ -137,4 +123,17 @@ Those marked * are not fully tested yet.
 .. [#]   Venkatesan, Ragav, and Baoxin Li. "Diving deeper into mentee networks." arXiv preprint 
          arXiv:1604.08220 (2016).
          
+.. _theano: http://deeplearning.net/software/theano/ 
+.. _GitHub: https://github.com/ragavvenkatesan/yann
+.. _Ragav Venkatesan: http://www.public.asu.edu/~rvenka10/
+.. _Visual Representaiton and Processing Group: http://www.public.asu.edu/~bli24/Research.html
+.. _Lasagne: https://github.com/Lasagne/Lasagne
+.. _Keras: http://keras.io/
+.. _Caffe: http://caffe.berkeleyvision.org/
+.. _Blocks: https://blocks.readthedocs.io/en/latest/
+.. _theano tutorials: http://deeplearning.net/software/theano/tutorial/examples.html 
+.. _skdata's: https://jaberg.github.io/skdata/
+.. _Fuel: https://github.com/mila-udem/fuel
+.. _Sebastien Bubeck's: https://blogs.princeton.edu/imabandit/2013/04/01/acceleratedgradientdescent/
+