Updated documentation to add relevant pages for releasE

Signed-off-by: Alejandro Saucedo <axsauze@gmail.com>

docs/index.rst

@@ -33,9 +33,19 @@ Documentation Index (as per sidebar)
     :caption: Python Documentation:
 
     Python Package Overview <overview/python-package>
-    Python Examples <overview/python-examples>
     Python Class Documentation & Reference <overview/python-reference>
 
+.. toctree::
+    :titlesonly:
+    :caption: Examples:
+
+    Python Examples <overview/python-examples>
+    C++ Examples <overview/advanced-examples>
+    Android Mobile App Integration <overview/mobile-android>
+    Game Engine Godot Integration <overview/game-engine-godot>
+    Example Benchmark with Matrix Multiplication <overview/matmul-benchmark>
+    Convolutional Neural Network (CNN) Simple Upscale <overview/convolutional-net>
+
 .. toctree::
     :titlesonly:
     :caption: Advanced Concepts & Deep Dives:
@@ -43,7 +53,5 @@ Documentation Index (as per sidebar)
     CI, Docker Images Docs & Tests <overview/ci-tests>
     Variable Types for Tensors, and Push/Spec Constants <overview/variable-types>
     Asynchronous & Parallel Operations <overview/async-parallel>
-    Mobile App Integration (Android) <overview/mobile-android>
-    Game Engine Integration (Godot Engine) <overview/game-engine-godot>
     Code Index <genindex>
 

docs/overview/convolutional-net.rst

@@ -0,0 +1,3 @@
+
+.. mdinclude:: ../../examples/neural_network_vgg7/README.md
+

docs/overview/matmul-benchmark.rst

@@ -0,0 +1,12 @@
+
+.. mdinclude:: ../../examples/python_naive_matmul/README.md
+
+
+Implementation Overview
+================
+
+The benchmark can be found in the `benchmark.py` file in the repo, which is outlined below. This file runs a naive implementation of the three matrix multiplication implementations to evaluate the performance of each.
+
+.. literalinclude:: ../../examples/python_naive_matmuln/benchmark.py
+   :language: python
+

docs/overview/raspberry-pi.rst

@@ -0,0 +1,3 @@
+
+.. mdinclude:: ../../examples/pi4_mesa_build/README.md
+

examples/neural_network_vgg7/.gitignore

@@ -1,3 +1,2 @@
 model-kipper
 model.json
-out.png

examples/neural_network_vgg7/README.md

@@ -1,6 +1,17 @@
-# Waifu2x VGG7 implementation
+# Convolutional Neural Network (CNN) VGG7 implementation
 
-This demonstrates performing image upscaling using Python and kompute.
+This example provides an implementation of a convolutional neural network (CNN) that enables for image resolution upscaling, which means that images can improve their quality through purely the machine learning implementation.
+
+This example demonstrates performing image upscaling using Kompute on the test image below.
+
+In this example we will be doing the following:
+
+* Import pre-trained model
+* Create Kompute code that loads model weights
+* Create Kompute shader that performs inference on image
+* Run model against image to perform upscale
+
+## Import pre-trained model
 
 To import the no-noise-compensation VGG7 model (into `model-kipper`):
 
@@ -11,7 +22,27 @@ python3 import_vgg7.py model.json
 
 Other models from the vgg\_7 set (such as `https://raw.githubusercontent.com/nagadomi/waifu2x/master/models/vgg_7/photo/noise0_model.json`) can be subsituted as desired.
 
-To execute that model (no tiling is performed, so be careful about image sizes):
+## Create code that loads model weights
+
+We implement the kompute logic under run_vgg7 that loads the model weights and coordinates the execution of the inference.
+
+## Create Kompute shader that performs inference on image
+
+Similarly, we created a compute shader that performs an inference iteration on an image provided to perfrom upscaling.
+
+## run model against image to perfrom upscale
+
+We now execute model against an image created by us to show how upscaling works. The image used will be the one below:
+
+![](w2wbinit.png)
+
+To execute that model no tiling is performed, so be careful about image sizes.
+
+We can now run the command below to perform inference against the image blow.
 
 `python3 run_vgg7.py w2wbinit.png out.png`
 
+This would successfully upscale the resolution using the machine learning model, and the result is below:
+
+![](out.png)
+