Deep Big Data Analytics

In the previous chapter, we established the fundamental theory of artificial neural networks (ANNs) and how they emulate human brain structure for generating output based on a set of inputs with the help of interconnected nodes. The nodes are arranged in three types of layers: input, hidden, and output. We understood the basic and mathematical concepts of how the input signal is carried through to the output layer and the iterative approach that ANNs take for training weights on neuron connections. Simple neural networks with one or two hidden layers can solve very rudimentary problems. However, in order to meaningfully utilize ANNs for real-world problems, which involve hundreds or thousands of input variables, involve more complex models, and require the models to store more information, we need more complex structures that are realized with large numbers...