a.k.a CNN or ConvNets
Sparse Interactions
Convolutional networks typically have sparse interactions, which is accomplished by making the kernel smaller than the input. For example, when processing an image, the input image might have thousands or millions of pixels, but we can detect small, meaningful features such as edges with kernels that occupy only tens or hundreds of pixels.
Parameter Sharing
Parameter sharing refers to using the same parameter for more than one function in a model. Ina convolutional neural net, each member of the kernel is used at every position of the input. The parameter sharing used by the convolution operation means that rather than learning a separate set of parameters for every location, we learn only one set
filters, convolutions, padding, strides, pooling
CNN architectures
Residual Learning or ResNets