The Convolutional Neural Network in this example is classifying images live in your browser using Javascript, at about 10 milliseconds per image. It takes an input image and transforms it through a series of functions into class probabilities at the end. The transformed representations in this visualization can be loosely thought of as the activations of the neurons along the way. The parameters of this function are learned with backpropagation on a dataset of (image, label) pairs. This particular network is classifying CIFAR-10 images into one of 10 classes and was trained with ConvNetJS. Its exact architecture is [conv-relu-conv-relu-pool]x3-fc-softmax, for a total of 17 layers and 7000 parameters. It uses 3x3 convolutions and 2x2 pooling regions. By the end of the class, you will know exactly what all these numbers mean.