Artificial Intelligence for Big Data
by Anand Deshpande, Manish Kumar, Albenzo Coletta, Giancarlo Zaccone
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 ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Read now
Unlock full access