In essence, ANN is used for multivariate regression (MVR) problems. This means, you have two or more input parameters (independent variables) that may or may not affect one or more output variables (response variables) and you want to figure out the functional relationship between the input and the output. How does ANN solve this problem? Well, it creates three distinct sets of layers, each including a certain number of 'nodes' (or neurons).
The figure above depicts the three types of layers. I say 'types of layers', because There can only be one input and one output layer, but multiple hidden layers are allowed. Having many hidden layers improves the model, but typically one or two layers are enough. There is also a compensation of exponential increase in computational time when increasing the number of hidden layers.
Multivariate regression (MVR) problems have been around for quite a while, and before the wonderful inventions we call computers, statisticians used to do everything by hand. Yikes! But that also means alternatives to ANN exist. One really old but still-used method is called Response Surface Methodology (RSM) which uses second degree polynomials to approximate the functional relationship.
Machine learning (ML) has opened a new door to a wide variety of solutions to MVR problems. In a tl;dr form, ML uses datasets from experiments or simulations, and can perform either 'supervised' or 'unsupervised' learning.
With supervised learning, data are classified, allowing errors of predictions to be calculated. With unsupervised learning, there is no labels to the data and therefore only draw inferences.
ANN can be used for both supervised and unsupervised learning.
An alternative to ANN exists, and it is called Support Vector Machines (SVM). SVM only supports supervised learning. (I don't yet know enough about SVMs yet to write more about it, sorry)
More on the two types of learning for ANN will be covered in my next post, so keep an eye out! :)
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