Designing an NN
DL relies on NNs, which consist of a few key building blocks, which in turn can be configured in a multitude of ways. In this section, we introduce how NNs work and illustrate their most important components used to design different architectures.
(Artificial) NNs were originally inspired by biological models of learning like 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 NN 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: even if the number of neurons used in NNs continued to double every year since their inception in the 1950s, they would only reach the scale of the human brain around 2050.
We will also explain how backpropagation, often simply called backprop, uses gradient information (the value of the partial derivative of the...
