What are convolutional layers in neural networks?

What are convolutional layers in neural networks?

Convolutional layers are specialized layers in neural networks designed to process data with a grid-like topology, such as images.

How do they differ from fully connected layers?

Unlike fully connected layers, convolutional layers use filters to scan input data, capturing spatial hierarchies and reducing parameters.

Can you explain how the filters work?

Filters, or kernels, slide over the input data, performing convolution operations to extract features like edges or textures.

What is the significance of pooling layers in this context?

Pooling layers reduce the spatial dimensions of the data, helping to decrease computation and control overfitting.