 A toy example of a deep convolutional net trained to recognize objects in images will help illustrate some of the details. The input to such a net consists of a digitized scene with red, green, and blue (RGB) values for the intensity of colors in each pixel. This input layer is fed to a layer of filter units, which are connected only to a small window of input pixels. Filter units detect specific, local features of the image using an operation called convolution. For example, they might find edges by noting where differences in the intensity of nearby pixels are the greatest. Outputs of these units are then passed to rectified linear units (or “ReLU” nodes), which only pass along activations from the filter nodes that exceed a certain threshold. ReLU units send their signals to a pooling layer, which collects data from many ReLU units and only passes along the most-activated features for each location. The result of this sandwich of convolution-ReLU-pooling layers is a “feature map”, which marks all and only the most salient features detected at each location across the whole image. This feature map can then be sent to a whole series of such sandwiches to detect larger and more abstract features. For example, one sandwich might build lines from edges, the next angles from lines, the next shapes from lines and angles, and the next objects from shapes. A final, fully-connected classification layer is then used to assign labels to the objects detected in the most abstract feature map delivered by the penultimate layer.