Defining the core application of neural networks

A common problem in neural networks is, that the newbies are not sure, for what purpose they can utilize the technology in a meaningful way. The reason is, that neural networks are usually described from it's own perspective which has to do with a certain neuron layout, the weights, a training algorithm or a certain software framework which can be programmed in Python or Java. But this description doesn't answer the question what the meaning of neural networks is.

With a bit research in the existing papers it's possible to simplify neural networks to it's core feature which is called “regression analysis”. A regression analysis is usually done with the SPSS statistics package. The idea, that a dataset contains of input and output variables, and the mathematical model is able to predict the outcome for interpolated input values. The most simple form of a linear regression model can be realized in MS-Excel and here, the model contains of a simple line which goes through existing datapoints. Let me give an example:

In the MS-Excel sheet the dataset: 2,4,6,8,10 is given. If we are visualizing the information in a plot and draw a line through the dots, it's possible to interpolate the missing point. The model can answer the question what the output for new input signal will be. It can predict, that between 4 and 6 the number 5 is there and that on the right side, the next datapoint will become 12.

This kind of regression anaylsis can be solved with neural networks very well. Much better than with MS-Excel or SPSS. The advantage is, that neural networks fit to nonlinear data with more than a single input variable. The training process means, that the existing dataset is converted into the model. Then the neural network is able to predict new output values for unseen input values. It's some kind of advanced interpolation mechanism.

This understanding of neural networks is important because in the task “using neural networks for regression analysis” no or only a few problems will become obvious. That means, no matter which kind of dataset is given, the neural network is able to do the regression task for all of them. This is some kind of core functionality, which fits well to what neural networks can provide to the user.

Sometimes, neural networks are used to do other task outside of regression analysis. And in this domain the most problems become obvious. It will become unclear, how to do the task exactly, and if the model was trained it doesn't work to fulfill the needs of the user.