corrections from review

docs/cugraph/source/tutorials/basic_cugraph.md

@@ -1,28 +1,32 @@
 ## Required hardware/software
 
-CuGraph is part of [Rapids](https://docs.rapids.ai/user-guide)
-It has the following system requirements
+CuGraph is part of [Rapids](https://docs.rapids.ai/user-guide) and has the following system requirements:
  * NVIDIA GPU, Volta architecture or later, with [compute capability](https://developer.nvidia.com/cuda-gpus) 7.0+
  * CUDA 11.2, 11.4, 11.5, 11.8, 12.0 or 12.2
  * Python version 3.9, 3.10, or 3.11
- * NetworkX >= version 3.0 (version 3.3 or higher recommended) **This if for use of nx-cuGraph, [see below](#cugraph-using-networkx-code).**
+ * NetworkX >= version 3.3 or higherin order to use use [NetworkXCongig](https://networkx.org/documentation/stable/reference/backends.html#module-networkx.utils.configs) **This is required for use of nx-cuGraph, [see below](#cugraph-using-networkx-code).**
 
 ## Installation
-The latest RAPIDS System Requirements documentation is located [here](https://docs.rapids.ai/install#system-req)
-This includes several ways to set up for cuGraph
+The latest RAPIDS System Requirements documentation is located [here](https://docs.rapids.ai/install#system-req).
+
+This includes several ways to set up cuGraph
 * From Unix
     * [Conda](https://docs.rapids.ai/install#wsl-conda)
     * [Docker](https://docs.rapids.ai/install#wsl-docker)
     * [pip](https://docs.rapids.ai/install#wsl-pip)
-* To use RAPIDS in windows you must install [WSL2](https://learn.microsoft.com/en-us/windows/wsl/install)
+
+* In windows you must install [WSL2](https://learn.microsoft.com/en-us/windows/wsl/install) and then choose one of the following:
     * [Conda](https://docs.rapids.ai/install#wsl-conda)
     * [Docker](https://docs.rapids.ai/install#wsl-docker)
     * [pip](https://docs.rapids.ai/install#wsl-pip)
-Build From Source
+
+* Build From Source
+
+To build from source, check each RAPIDS GitHub README for set up and build instructions. Further links are provided in the [selector tool](https://docs.rapids.ai/install#selector). If additional help is needed reach out on our [Slack Channel](https://rapids-goai.slack.com/archives/C5E06F4DC).
 
 ## CuGraph Using NetworkX Code
 While the steps above are required to use the full suite of cuGraph graph analytics, cuGraph is now supported as a NetworkX backend using [nx-cugraph](https://docs.rapids.ai/api/cugraph/nightly/nx_cugraph/nx_cugraph/).
-This is much simpler but limits users to the current but growing list of suppored algorithms.
+Nx-cugraph offers those with existing nwtworkX code a zero code change option with a growing list of supported algorithms.
 
 
 ## Cugraph API demo