What is CNN architecture and how does it work?

What is CNN architecture and how does it work?

CNN, or Convolutional Neural Network, is a deep learning model primarily used for image processing. It consists of layers that automatically detect features from images.

Can you explain the different layers in a CNN?

Sure! A typical CNN has several layers: convolutional layers for feature extraction, pooling layers for down-sampling, and fully connected layers for classification.

What is the purpose of pooling layers?

Pooling layers reduce the spatial dimensions of the feature maps, which helps to decrease the computational load and control overfitting.

How does CNN compare to traditional neural networks?

CNNs are specifically designed for grid-like data such as images, allowing them to capture spatial hierarchies, unlike traditional neural networks that treat inputs as flat.