Deep convolutional neural network — DCNN

A deep convolutional neural network (DCNN) consists of many neural network layers. Two different types of layers, convolutional and pooling, are typically alternated. The depth of each filter increases from left to right in the network. The last stage is typically made of one or more fully connected layers:

There are three key intuitions beyond ConvNets:

• Local receptive fields
• Shared weights
• Pooling

Let's review them.

Local receptive fields

If we want to preserve spatial information, then it is convenient to represent each image with a matrix of pixels. Then, a simple way to encode the local structure is to connect a submatrix of adjacent input neurons into one single hidden neuron belonging to the next layer. That single hidden neuron represents one local receptive field. Note that this operation is named convolution and it gives the name to this type of network.

Of course, we can encode more information by having overlapping submatrices. For instance, let...