Edit index page for new howto section (#578)

doc/sphinx/source/glossary.rst

@@ -92,3 +92,8 @@ Glossary
       Tensors are maps from *coordinates* to scalar values, all of the
       same type, called the *element type* of the tensor.
 
+   model description
+
+      A description of a program's fundamental operations that are 
+      used by a framework to generate inputs for computation.   
+

doc/sphinx/source/howto/execute.rst

-.. execute.rst
+.. execute-cmp.rst
 
 ######################
 Execute a Computation
@@ -7,10 +7,10 @@ Execute a Computation
 This section explains how to manually perform the steps that would normally be 
 performed by a framework :term:`bridge` to execute a computation. Intel® nGraph 
 library is targeted toward automatic construction; it is far easier for a 
-processing unit (GPU, CPU, or NNP) to run a computation than it is for a user 
-to map out how that computation happens. Unfortunately, things that make by-hand 
-graph construction simpler tend to make automatic construction more difficult, 
-and vice versa.
+processing unit (GPU, CPU, or an `Intel Nervana NNP`_) to run a computation than 
+it is for a user to map out how that computation happens. Unfortunately, things 
+that make by-hand graph construction simpler tend to make automatic construction 
+more difficult, and vice versa.
 
 Here we will do all the bridge steps manually. The :term:`model description` 
 we're explaining is based on the :file:`abc.cpp` file in the ``/doc/examples/`` 
@@ -25,7 +25,7 @@ user) must be able to carry out in order to successfully execute a computation:
 * :ref:`invoke_cmp`
 * :ref:`access_outputs`
 
-The final code is a the end of this page, on :ref:`all_together`.
+The final code is at the :ref:`end of this page <all_together>`.
 
 
 .. _define_cmp:
@@ -64,8 +64,8 @@ deallocated when they are no longer needed. A brief summary of shared
 pointers is given in the glossary.
 
 Every node has zero or more *inputs*, zero or more *outputs*, and zero or more 
-*attributes*.  The specifics for each :cpp::type:: permitted on a core 
-``Op``-specific basis can be discovered in :doc:`ops` docs. For our 
+*attributes*.  The specifics for each ``type`` permitted on a core ``Op``-specific 
+basis can be discovered in our :doc:`../ops/index` docs. For our 
 purpose to :ref:`define a computation <define_cmp>`, nodes should be thought of 
 as essentially immutable; that is, when constructing a node, we need to supply 
 all of its inputs. We get this process started with ops that have no inputs, 
@@ -233,4 +233,5 @@ Put it all together
 
 
 
-.. _Intel MKL-DNN: https://01.org/mkl-dnn
+.. _Intel MKL-DNN: https://01.org/mkl-dnn
\ No newline at end of file
+.. _Intel Nervana NNP: https://ai.intel.com/intel-nervana-neural-network-processors-nnp-redefine-ai-silicon/
\ No newline at end of file

doc/sphinx/source/howto/index.rst

@@ -3,26 +3,43 @@
 How to 
 ======
 
-.. note:: This section is aimed at intermediate users of Intel nGraph library. 
-   It assumes a developer has understanding of the concepts in the previous 
-   sections. It does not assume knowledge of any particular frontend framework. 
-
-The "How to" articles in this section explain how to do specific tasks with 
-Intel nGraph. The recipes are all framework agnostic; in other words, any 
-frontend framework that wishes to access the optimizations inherent in nGraph 
-will either need to do these things programatically through the framework, or to
-provide documentation for the user. Our primary audience is users who have 
-already decided that they want the performance optimizations available through 
-the nGraph library's management of custom backends. 
+.. note:: This section is aimed at intermediate-level developers working with
+   the nGraph library. It assumes a developer has understanding of the concepts 
+   in the previous sections. It does not assume knowledge of any particular 
+   frontend framework. 
+
+The "How to" articles in this section explain how to do specific tasks with the 
+Intel nGraph library. The recipes are all framework agnostic; in other words, 
+if an entity (framework or user) wishes to make use of target-based computational 
+resources, it can either:
+
+* Do the tasks programatically through the framework, or 
+* Provide a clear model definition with documentation for the computational 
+  resources needed. 
+  
+Since our primary audience is developers who are pushing the boundaries of deep 
+learning systems, we go beyond the use of deep learning primitives, and include 
+APIs and documentation for developers who want the ability to write programs 
+that use custom backends. For example, we know that GPU resources can be useful 
+backends for *some* kinds of algorithmic operations while they impose inherent 
+limitations and slow down others. We are barely scraping the surface of what is 
+possible for a hybridized approach to many kinds of training and inference-based 
+computational tasks. 
+
+One of our goals with the nGraph project is to enable developers with tools to 
+build programs that quickly access and process data with or from a breadth of 
+edge and network devices.  Furthermore, we want them to be able to make use of 
+the best kind of computational resources for the kind of data they are processing,
+after it has been gathered.
 
 To get started, we've provided a basic example for how to execute a computation 
 that can run on an nGraph backend; this is analogous to a framework bridge.  
 
-This section is under development; it will eventually contain articles targeted
-toward data scientists, algorithm designers, framework developers, and backend
-engineers -- anyone who wants to pivot on our examples and experiment with the 
-variety of hybridization and performance extractions available through the 
-nGraph library.    
+This section is under development; it will eventually be populated with more 
+articles geared toward data scientists, algorithm designers, framework developers, 
+backend engineers, and others.  We welcome contributions from the community and
+invite you to experiment with the variety of hybridization and performance 
+extractions available through the nGraph library.    
 
 .. toctree::
    :maxdepth: 1