In Chapter 11 we studied artificial neural networks (NNs) , a supervised learning paradigm that mimics the way neurons in our brain work. The learning process in NNs consists of approximating a function described in a tabular manner via a training data set containing features of objects (inputs to the function to be approximated) and their corresponding classification (outputs of the function).
As described before, NNs are capable of learning a function described in a training data set by adjusting the set of weights linking their neurons. At the ...
