Convolutional Deep Nets

• One of the other nets is a convlutional network.
• These take advantage of receptive fields (like natural vision systems).
• So, a hidden layer has a 2D topology, with inputs from one layer to the next being restricted by area.
• They also take advantage of pooling.
• This merely combines some inputs to reduce the data being procesed.
• The overall layers are feedforward, but they can break apart, and recombine.