Update embedded_ml_exercise.qmd

embedded_ml_exercise.qmd

@@ -294,7 +294,7 @@ or rotating the images).
 Under the rood, here you can see how Edge Impulse implements a data
 Augmentation policy on your data:
 
-```{{python}}
+```python
 # Implements the data augmentation policy
 def augment_image(image, label):
     # Flips the image randomly
@@ -445,7 +445,7 @@ on the OpenMV IDE.
 GitHub,]{.underline}](https://github.com/Mjrovai/Arduino_Nicla_Vision/blob/main/Micropython/nicla_image_classification.py)
 or modify it as below:
 
-```{{python}}
+```python
 # Marcelo Rovai - NICLA Vision - Image Classification
 # Adapted from Edge Impulse - OpenMV Image Classification Example
 # @24Aug23
@@ -548,7 +548,7 @@ For that, we should [[upload the code from
 GitHub]{.underline}](https://github.com/Mjrovai/Arduino_Nicla_Vision/blob/main/Micropython/nicla_image_classification_LED.py)
 or change the last code to include the LEDs:
 
-```{{python}}
+```python
 # Marcelo Rovai - NICLA Vision - Image Classification with LEDs
 # Adapted from Edge Impulse - OpenMV Image Classification Example
 # @24Aug23
@@ -724,4 +724,4 @@ projects around vision in several areas, such as:
     > human-machine interaction.
 
 -   **Content Recommendation**: Image-based recommendation systems in
-    > e-commerce.
+    > e-commerce.