Going Deep

In this chapter, we have so far been working with relatively ‘classical’ machine learning models, which rely on a variety of different approaches to learn from data often motivated by mathematical arguments from researchers. These algorithms in general are not modelled on any biological theory of learning and are at their heart motivated by the statistics and mathematics. A slightly different approach that the reader will likely be aware of, and that we have met briefly in the sections on Learning About Learning, is that taken by Artificial Neural Networks (ANNs), which originated in the 1950s and were based on idealized models of neuronal activity in the brain. The core concept of an ANN is that through connecting relatively simple computational units called neurons (modelled on biological neurons) we can build systems that can effectively model any mathematical function. The neuron in this case is a small component of the system which will return a 0 or 1 result...