Varieties of networks: convolution and recursive
Up until now, we’ve primarily discussed the basics of neural networks by referencing feedforward networks, where every input is connected to every output in each layer. While these feedforward networks are useful for illustrating how deep networks are trained, they are only one class of a broader set of architectures used in modern applications, including generative models. Thus, before covering some of the techniques that make training large networks practical, let’s review these alternative deep models.
Networks for seeing: convolutional architectures
As noted at the beginning of this chapter, one of the inspirations for deep neural network models is the biological nervous system. As researchers attempted to design computer vision systems that would mimic the functioning of the visual system, they turned to the architecture of the retina, as revealed by physiological studies by neurobiologists David Hubel and...
