Dear Doctor

"CNNs have unique layers called convolutional layers that separate them from RNNs and other neural networks. Within a convolutional layer, the input is transformed before being passed to the next layer. A CNN transforms the data by using filters."

Hello,

I just wanted to add a response with a bit more detail. The following was generated when I posed your question to MS Copilot. To be clear, this response was not generated by me, but I think shows how new generative AI tools can be a great starting point for learning about different topics!

A Convolutional Neural Network (CNN) and a traditional neural network, often referred to as a Fully Connected Neural Network (FCNN), differ primarily in their structure and the types of problems they’re designed to solve.

Traditional Neural Networks (FCNNs):

• FCNNs are the oldest and most common type of neural networks.
• They consist of interconnected neurons or nodes, where each node receives inputs and generates a single output.
• The output of a neuron is calculated by applying an activation function to the weighted sum of all the inputs and weights of the connections.
• FCNNs are general-purpose networks used for a variety of tasks, but they do not take into account the spatial structure of the data.

Convolutional Neural Networks (CNNs):

• CNNs are a subclass of neural networks that have at least one convolution layer.
• They are specifically designed for processing grid-like topology data such as an image.
• CNNs are great for capturing local information (e.g., neighbor pixels in an image) as well as reducing the complexity of the model.
• A convolution unit in a CNN receives its input from multiple units from the previous layer which together create a proximity.
• The convolution units (as well as pooling units) reduce the number of units in the network, meaning there are fewer parameters to learn which reduces the chance of overfitting.
• They consider the context/shared information in the small neighborhoods. This feature is very important in many applications such as image, video, text, and speech processing/mining.

In the context of computer vision, CNNs have proven to be more effective than traditional neural networks because they can take advantage of the spatial structure of the image and learn hierarchical patterns, which is a key aspect of human visual perception. This makes CNNs particularly suited for tasks like image recognition and object detection.

References:

• https://www.baeldung.com/cs/convolutional-vs-regular-nn
• https://ai.stackexchange.com/questions/5546/what-is-the-difference-between-a-convolutional-neural-network-and-a-regular-neur