Added to changelog

Signed-off-by: Alejandro Saucedo <axsauze@gmail.com>
KomputeProject · Sep 12, 2021 · 21d9939 · 21d9939
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diff --git a/docs/overview/matmul-benchmark.rst b/docs/overview/matmul-benchmark.rst
@@ -3,10 +3,12 @@
 
 
 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
+
+.. literalinclude:: ../../examples/python_naive_matmul/benchmark.py
  :language: python
 
+
diff --git a/examples/neural_network_vgg7/README.md b/examples/neural_network_vgg7/README.md
@@ -30,11 +30,11 @@ We implement the kompute logic under run_vgg7 that loads the model weights and c
 
 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
+## 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)
+![](https://raw.githubusercontent.com/KomputeProject/kompute/master/examples/neural_network_vgg7/w2wbinit.png)
 
 To execute that model no tiling is performed, so be careful about image sizes.
 
@@ -44,5 +44,6 @@ We can now run the command below to perform inference against the image blow.
 
 This would successfully upscale the resolution using the machine learning model, and the result is below:
 
-![](out.png)
+![](https://raw.githubusercontent.com/KomputeProject/kompute/master/examples/neural_network_vgg7/out.png)
+