Neural Networks Rethought
Although the word “Neural” would spark quite a biological discussion, it would also spark a conversation about Artificial Intelligence and Neural Networks. Since the dawn of intelligent beings, the humankind has wondered about how our brains work. And although we have gotten quite far in explaining the reactions that occur in the brain, we are nowhere near being able to fully understand the brain. Similar to the brain, we have barely scratched the surface of neural networks and their possibilities. An artificial neural network (or simply neural network) is an encoded network of nodes which act as “neurons” while mapping out a network of nodes similar to the human brain. The simplest definition of a neural network is stated by Robert Heicht Nelson, the inventor of the first neurocomputer.
“. . . . a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.”
Neural networks consist of at least 3 node-layers. The input layer is responsible for identifying and defining the input data. The data then reaches “hidden layers.” Such layers have the name hidden layer as it is quite difficult to distinguish each hidden layer. The hidden layer transfers the input data to viable output data. There can be infinitely many hidden layers. The final layer, known as the output 1 layer, is responsible for outputting the new data. This system of networks have many applications in developing algorithms or recognition software.