diff --git a/README.rst b/README.rst
index a202260..f452146 100644
--- a/README.rst
+++ b/README.rst
@@ -18,13 +18,60 @@
 PyProf - PyTorch Profiling tool
 ===============================
 
-    **NOTE: You are currently on the r20.11 branch which tracks stabilization
-    towards the release. This branch is not usable during stabilization.**
-
 .. overview-begin-marker-do-not-remove
 
+PyProf is a tool that profiles and analyzes the GPU performance of PyTorch
+models. PyProf aggregates kernel performance from `Nsight Systems
+<https://developer.nvidia.com/nsight-systems>`_ or `NvProf
+<https://developer.nvidia.com/nvidia-visual-profiler>`_.
+
+What's New in 3.6.0
+-------------------
+
+* PyProf overhead was reduced to improve runtime performance
+
+  * Improved database query from Nsight Systems
+  * Refactored nvmarker.py
+
+Known Issues
+------------
+
+* Forward-Backward kernel correlation heuristics do not work correctly with 
+  PyTorch 1.6. Recommended work arounds include:
+
+  * Use with PyTorch 1.5
+  * Use DLProf in the `20.10 NGC Pytorch container <https://ngc.nvidia.com/catalog/containers/nvidia:pytorch>`_
+
+Features
+--------
+
+* Identifies the layer that launched a kernel: e.g. the association of 
+  `ComputeOffsetsKernel` with a concrete PyTorch layer or API is not obvious.
+
+* Identifies the tensor dimensions and precision: without knowing the tensor 
+  dimensions and precision, it's impossible to reason about whether the actual 
+  (silicon) kernel time is close to maximum performance of such a kernel on 
+  the GPU. Knowing the tensor dimensions and precision, we can figure out the 
+  FLOPs and bandwidth required by a layer, and then determine how close to 
+  maximum performance the kernel is for that operation.
+
+* Forward-backward correlation: PyProf determines what the forward pass step 
+  is that resulted in the particular weight and data gradients (wgrad, dgrad), 
+  which makes it possible to determine the tensor dimensions required by these
+  backprop steps to assess their performance.
+
+* Determines Tensor Core usage: PyProf can highlight the kernels that use 
+  `Tensor Cores <https://developer.nvidia.com/tensor-cores>`_.
+
+* Correlate the line in the user's code that launched a particular kernel (program trace).
+
 .. overview-end-marker-do-not-remove
 
+The current release of PyProf is 3.6.0 and is available in the 20.11 release of
+the PyTorch container on `NVIDIA GPU Cloud (NGC) <https://ngc.nvidia.com>`_. The 
+branch for this release is `r20.11
+<https://github.com/NVIDIA/PyProf/tree/r20.11>`_.
+
 Quick Installation Instructions
 -------------------------------
 
@@ -46,7 +93,7 @@ Quick Installation Instructions
 
 * Should display ::
 
-   pyprof            3.6.0.dev0
+   pyprof            3.6.0
 
 .. quick-install-end-marker-do-not-remove
 
@@ -75,5 +122,57 @@ Quick Start Instructions
 
 .. quick-start-end-marker-do-not-remove
 
+Documentation
+-------------
+
+The User Guide can be found in the 
+`documentation for current release 
+<https://docs.nvidia.com/deeplearning/frameworks/pyprof-user-guide/index.html>`_, and 
+provides instructions on how to install and profile with PyProf.
+
+A complete `Quick Start Guide <https://docs.nvidia.com/deeplearning/frameworks/pyprof-user-guide/quickstart.html>`_ 
+provides step-by-step instructions to get you quickly started using PyProf.
+
+An `FAQ <https://docs.nvidia.com/deeplearning/frameworks/pyprof-user-guide/faqs.html>`_ provides
+answers for frequently asked questions.
+
+The `Release Notes 
+<https://docs.nvidia.com/deeplearning/frameworks/pyprof-release-notes/index.html>`_
+indicate the required versions of the NVIDIA Driver and CUDA, and also describe 
+which GPUs are supported by PyProf
+
+Presentation and Papers
+^^^^^^^^^^^^^^^^^^^^^^^
+
+* `Automating End-toEnd PyTorch Profiling <https://developer.nvidia.com/gtc/2020/video/s21143>`_.
+   * `Presentation slides <https://developer.download.nvidia.com/video/gputechconf/gtc/2020/presentations/s21143-automating-end-to-end-pytorch-profiling.pdf>`_.
+
+Contributing
+------------
+
+Contributions to PyProf are more than welcome. To
+contribute make a pull request and follow the guidelines outlined in
+the `Contributing <CONTRIBUTING.md>`_ document.
+
+Reporting problems, asking questions
+------------------------------------
+
+We appreciate any feedback, questions or bug reporting regarding this
+project. When help with code is needed, follow the process outlined in
+the Stack Overflow (https://stackoverflow.com/help/mcve)
+document. Ensure posted examples are:
+
+* minimal – use as little code as possible that still produces the
+  same problem
+
+* complete – provide all parts needed to reproduce the problem. Check
+  if you can strip external dependency and still show the problem. The
+  less time we spend on reproducing problems the more time we have to
+  fix it
+
+* verifiable – test the code you're about to provide to make sure it
+  reproduces the problem. Remove all other problems that are not
+  related to your request/question.
+
 .. |License| image:: https://img.shields.io/badge/License-Apache2-green.svg
    :target: http://www.apache.org/licenses/LICENSE-2.0