How do convolutional neural networks work?

Can you explain how convolutional neural networks work?

Sure! Convolutional Neural Networks (CNNs) are a class of deep learning models primarily used for image processing. They work by applying convolutional layers that scan the input image with filters to detect features.

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.

How do the filters in CNNs learn?

Filters in CNNs learn through a process called backpropagation, where the network adjusts the filter weights based on the error of the predictions compared to the actual labels.

Can CNNs be used for tasks other than image recognition?

Yes, CNNs can also be applied to tasks like video analysis, natural language processing, and even audio recognition.