In the human brain, the interconnected neurons form a network. Simple information transits and processed in the brain before giving direction to limbs. The process is automatic; same goes with simple neural network. The variables are entered as inputs in a simple neural network, and after some calculation, the output is returned. The inputs can be image, number, graphics, text etc. The simplest neuron called Perceptron consists of two neurons in the input column and one neuron in the output column. It allows creating a simple classifier to distinguish two groups.
Simple Neural Network is a set of algorithms designed as per the human brain to recognize patterns. It interprets sensory data through a machine perception. The patterns it recognizes are numerical and contained in vectors. They can be images, sound, text, time-series, which are translated for recognition.
Self-learning Artificial Neuron Networks
Artificial neurons networks are placed in columns so that neuron of column n can be connected to neurons of column n+1 and n-1. Neural networks are usually read from left to right. Neurons have several inputs called dendrites, cell nucleus for processing and axon as output. Identifying variables and defining their relationship is not sufficient to make the whole neural network learn by itself. A lot of more data in the form of input and the expected output is needed.
When anyone enters an input in a neural network, it returns an output. The output may be right or wrong. During the learning phase, each input comes with a label that explains what output can be expected from a neural network. If the choice is proper, actual parameters are kept, and the next input is given. However, if they obtained output does not match the label, the parameters are changed. The parameters are the variables that can be changed during the learning phase. Such a process gives an idea about multiple possibilities when input is not guessed correctly. Backpropagation is done to determine which weight or variable should be modified. Then one needs to check how the output would behave according to a change in weight or parameter.
Artificial Neural network (ANN) does not restrict the input variables. It can easily handle data with high volatility and non-constant variance as it learns different latent relationships in the data without forcing any fixed relationship among neurons. This feature proves useful in financial time-series forecasting, where data fluctuate highly. ANNs have useful applications in medicine, security, banking/finance, government, agriculture and defense.
Each neural network learns at a different pace than others. The learning rate indicates the speed and way at which it modifies a variable or weight. Learning rate of 1 is considered good.
Use of Neural Network
Neural networks help to group unlabeled data according to similarities among example inputs. They can extract similar features fed to other algorithms for grouping and classification. Some neural networks allow modeling of nonlinear processes. They have useful applications in classification, clustering, machine translation, anomaly or fraud detection, forecasting, regression, pattern recognition, dimension reduction and decision-making. Neural networks are widely used in self-driving cars, character recognition, image compression, stock prediction and other real-life situations.