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python/README.md

-## nGraph Neural Network compiler
-
-[nGraph][ngraph_github] is Intel's open-source graph compiler and runtime for Neural Network models. Frameworks using nGraph to execute workloads have shown up to [45X](https://ai.intel.com/ngraph-compiler-stack-beta-release/) performance boost compared to native implementations.
-
-nGraph can be used directly thought it's [Python API][api_python] or [C++ API][api_cpp]. Alternatively it can be used through one of its frontends, e.g. [TensorFlow][frontend_tf], [MXNet][frontend_mxnet] and [ONNX][frontend_onnx].
+## nGraph Compiler stack
+
+[nGraph][ngraph_github] is an open-source graph compiler for Artificial 
+Neural Networks (ANNs). The nGraph Compiler stack provides an inherently 
+efficient graph-based compilation infrastructure designed to be compatible 
+with the many upcoming processors, like the Intel(R) Nervana(TM) Neural Network 
+Processor (Intel(R) Nervana(TM) NNP), while also unlocking a massive performance 
+boost on any existing hardware targets in your neural network: both GPUs 
+and CPUs. Using its flexible infrastructure, you will find it becomes 
+much easier to create Deep Learning (DL) models that can adhere to the 
+"write once, run anywhere" mantra that enables your AI solutions to easily
+go from concept to production to scale.
+
+Frameworks using nGraph to execute workloads have shown [up to 45X] performance 
+boost compared to native implementations.
+
+### Using the Python API 
+
+nGraph can be used directly with the [Python API][api_python] described here, or 
+with the [C++ API][api_cpp] described in the [core documentation]. Alternatively, 
+its performance benefits can be realized through a frontend such as 
+[TensorFlow][frontend_tf], [MXNet][frontend_mxnet], and [ONNX][frontend_onnx].
+You can also create your own custom framework to integrate directly with the 
+[nGraph Ops] for highly-targeted graph execution.
 
 ## Installation
 
-nGraph is available as binary wheels you can install from PyPI. nGraph binary wheels are currently tested on Ubuntu 16.04 and require a CPU with AVX-512 instructions, if you're using a different system, you may want to [build][ngraph_building] nGraph from sources.
+nGraph is available as binary wheels you can install from PyPI. nGraph binary 
+wheels are currently tested on Ubuntu 16.04. To build and test on other 
+systems, you may want to try [building][ngraph_building] from sources.
 
 Installing nGraph Python API from PyPI is easy:
 
@@ -14,7 +35,10 @@ Installing nGraph Python API from PyPI is easy:
 
 ## Usage example
 
-Using nGraph's Python API to construct a computation graph and execute a computation is simple. The following example shows how to create a simple `(A + B) * C` computation graph and calculate a result using 3 numpy arrays as input.
+Using nGraph's Python API to construct a computation graph and execute a 
+computation is simple. The following example shows how to create a minimal 
+`(A + B) * C` computation graph and calculate a result using 3 numpy arrays 
+as input.
 
 ```python
 import numpy as np
@@ -50,10 +74,13 @@ result = computation(value_a, value_b, value_c)
 print('Result = ', result)
 ```
 
+[up to 45X]: https://ai.intel.com/ngraph-compiler-stack-beta-release/
 [frontend_onnx]: https://pypi.org/project/ngraph-onnx/
 [frontend_mxnet]: https://pypi.org/project/ngraph-mxnet/ 
 [frontend_tf]: https://pypi.org/project/ngraph-tensorflow-bridge/
 [ngraph_github]: https://github.com/NervanaSystems/ngraph "nGraph on GitHub"
 [ngraph_building]: https://github.com/NervanaSystems/ngraph/blob/master/python/BUILDING.md "Building nGraph"
 [api_python]: https://ngraph.nervanasys.com/docs/latest/python_api/ "nGraph's Python API documentation"
-[api_cpp]: https://ngraph.nervanasys.com/docs/latest/howto/ 
+[api_cpp]: https://ngraph.nervanasys.com/docs/latest/backend-support/cpp-api.html
+[core documentation]: https://ngraph.nervanasys.com/docs/latest/core/overview.html
+[nGraph Ops]: http://ngraph.nervanasys.com/docs/latest/ops/index.html