Update CNN image with licensed version

Author: dominica-of

content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/Typical_cnn.png

@@ -15,13 +15,16 @@ You will train a lightweight CNN to classify images of the letters R, P, and S a
 ### What is a Convolutional Neural Network (CNN)?
 A Convolutional Neural Network (CNN) is a type of deep neural network primarily used for analyzing visual imagery. Unlike traditional neural networks, CNNs are designed to process pixel data by using a mathematical operation called convolution. This allows them to automatically and adaptively learn spatial hierarchies of features from input images, from low-level features like edges and textures to high-level features like shapes and objects.
 
-A convolutional neural network (CNN) is a deep neural network designed to analyze visual data using the *convolution* operation. CNNs learn spatial hierarchies of features - from edges and textures to shapes and objects - directly from pixels.
+![CNN architecture](Typical_cnn.png)
+
+Typical CNN architecture by Aphex34, licensed under 
+[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/).
 
 Common CNN applications include:
 
-- Image classification: identify the main object in an image, such as classifying a photo as a cat or dog
-- Object detection: locate specific objects in an image and draw bounding boxes
-- Facial recognition: identify or verify individuals based on facial features
+- Image classification: identifying the main object in an image, such as classifying a photo as a cat or dog
+- Object detection: locating specific objects in an image and draw bounding boxes
+- Facial recognition: identifying or verify individuals based on facial features
 
 For the rock-paper-scissors game, you use a tiny CNN to classify the letters R, P, and S as the corresponding hand gestures.