Introduction to neural networks
The topic of neural networks in the IT area and the issues of artificial intelligence have become very popular in recent years. Neural networks are not a new concept, as they were already popular in the 1970’s. However, their real development took place in the 21st century due to the technology’s huge leap forward. Neural networks are one of the areas of artificial intelligence (AI). The interest in neural networks is growing, thus forcing us to constantly develop and improve them.
How neural networks work?
In order to describe the way neural networks work, it is worth referring (in a certain simplification) to the way the human nervous system works. The characteristic of the functionality is acting like a biological system. Despite enormous progress and the use of innovative solutions, today’s networks are not able to act as well as the human brain, but it can not be ruled out, that in the future such an advanced stage of development will be reached.
Neural network structure
The neural network consist of a certain number of neurons. The simplest neural network is called perceptone, which consist of only one artificial neuron. Input data with assigned weight scales is sent to the perceptone – it determines the final result of a parameter. This set of data is later sent to the summation block. The summation block is just a pattern, an algorithm prepared by programmers. The sum of all inputs gives a result, which in today’s advanced types of artificial neurons gives the answer in the form of a real number. The result informs about the type of decision which was made based on the calculations.
The usage of neural networks
The development of AI means the development of neural networks. An unquestionable advantage of networks is that they have a wide range of applications along with the unlimited possibilities for further development. Another advantage of neural networks is that they deal well with large data sets, which are sometimes very problematic for people. What’s more, they are able to adapt to the new situation when new variables appear – most available on the market programs do not have this possibility. Neural networks’s ability to work on the basis of damaged data or on its fragments is still a field of development. This creates a situation when it can be compared to solving equations with many unknowns, which is finally solved by the network. Neural networks find application in growing number of areas, mainly in finance, medicine or technology. Neural networks will appear successively in areas that require solutions related to prediction, classification and control. They will find their application wherever creating scenarios or making decisions is based on many variables.