Figure from:Advanced Digital Auditing
Figure 11 Introduction to Advanced Information Technology Neural networks are vastly complex multi-layered networks. Similar to a human brain, a neural network encompasses several nodes (similar to a neuron) that are inter-connected which allows data to be passed between them. The neural network always encompasses an input layer and an output layer. In between the input and output layer there are multiple hidden layers. Some neural networks can encompass millions of hidden layers, whereas others only have 20. The hidden layers in a neural network pass on data from the input layer and provide a subsequent outcome to the output layer. Due to the complexity of neural networks it difficult, if not impossible, to understand what happens when data is passed between the nodes. Therefore, neural networks in all of their different shapes and sizes are considered a black box, meaning that we know the input and the output of the algorithm but not what happens during the processing of the data. How neural networks are structured strongly depends on the deep learning algorithm used to perform a task. In turn, research (Pouyanfar et al., 2018) has demonstrated that some types of deep learning algorithms are more suitable than others for a specific task. Hence, there is often a strong relation between the task at hand and the type of neural network employed to store the data. Roughly speaking neural networks can be divided into two groups: convolutional neural networks and recurrent neural networks. Convolutional neural networks are predominantly used for image recognition.
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