CNNs, or ConvNets have gotten a lot of attention in the last few years, mainly due to their major successes in the domain of computer vision. They are at the core of most computer vision systems nowadays, including self-driving cars and large-scale photo classification systems.
In some sense, CNNs are very similar to multilayer perceptron, which we have discussed in the previous chapter. These networks also build from the layers, but unlike MLP, which usually has all layers similar to each other, CNNs usually include many layers of different types. And the most important type of the layer is (surprise, surprise) the convolutional layer. Modern CNNs can be really deep—hundreds of different layers. Nevertheless, you can still see the whole network as one differentiable function that takes some input (usually raw values of image pixels), and produces some output (for example, class probabilities: 0.8 cat, 0.2 dog).