What are convolutional neural networks and how do they work?

What are convolutional neural networks and how do they work?

Convolutional Neural Networks (CNNs) are a class of deep learning algorithms primarily used for image processing. They work by applying convolutional layers that scan input images with filters to detect features like edges and textures.

What are the main components of a CNN?

The main components of a CNN include convolutional layers, pooling layers, and fully connected layers. Convolutional layers extract features, pooling layers reduce dimensionality, and fully connected layers make predictions.

Can you explain how the pooling layer functions?

Sure! The pooling layer reduces the spatial size of the representation, which decreases the number of parameters and computations in the network. It helps to make the detection of features invariant to scale and orientation.

What are some common applications of CNNs?

Common applications of CNNs include image classification, object detection, facial recognition, and medical image analysis.