The document discusses convolutional neural networks (CNNs). It explains that CNNs have convolutional layers and pooling layers, as well as fully connected layers. It describes three key aspects of CNNs: local receptive fields, subsampling, and shared weights. Local receptive fields allow a neuron to only be influenced by a small region of the input. Subsampling reduces the spatial resolution but increases the number of features. Shared weights enable the same pattern to be detected across the input. The document provides an overview of how CNNs work, from input to convolutional and pooling layers to fully connected output layers.