DL relies on neural networks, which consist of a few key building blocks, which in turn can be configured in a multitude of ways. In this section, we will introduce how neural networks work and illustrate the most important components used to design different architectures, including types of hidden and output units, cost functions, and various options to connect these components.
Neural networks, also called artificial neural networks, were inspired by biological models of learning as represented by the human brain, either in an attempt to mimic how it works and achieve similar success, or to gain a better understanding through simulation. Current neural network research draws less on neuroscience, not least since our understanding of the brain has not yet reached a sufficient level of granularity. Another constraint is overall size: while the number...
