What are the differences between CNNs and regular neural networks?

What are the differences between CNNs and regular neural networks?

Convolutional Neural Networks (CNNs) are specialized for processing grid-like data such as images, while regular neural networks are more general-purpose.

Why are CNNs better for image processing?

CNNs use convolutional layers that can capture spatial hierarchies in images, making them more effective for tasks like image recognition.

Can you give an example of a task where CNNs outperform regular neural networks?

Sure! CNNs excel in tasks like facial recognition and object detection, where understanding spatial relationships is crucial.

Are there any limitations to using CNNs?

Yes, CNNs require a large amount of labeled data and can be computationally intensive, which may not be feasible for all applications.