Neural networks are one of the options for the mathematical representation (or imitation) of reality, its software or hardware implementation. The structure of ANN functioning consists of the principles of work of structural and functional units of the nervous system of a living organism.
What is a “neural network”.
If earlier technological progress was considered an invention from the section of science fiction, today mankind is practically accustomed to the fact that cars are produced with an autopilot, and using a smartphone you can talk to artificial intelligence. Over the past decade, such innovations have become available thanks to neural networks.
They are a kind of set of hardware and software tools that are created to solve complex problems and process information, and are also capable of self-learning with a consistent increase in their own capabilities, in general, and productivity, in particular.
As a rule, ANNs are used to solve complex problems where it is necessary to logically deduce information based on given facts and processing rules. These include:
- Machine learning. If the system is given a million tasks of the same type, it will gradually learn to solve them on its own. This skill will remain in memory, and artificial intelligence will be able to apply it at any time to perform specific tasks.
- Robotics. A neural network is the main tool in the development of algorithms for the “brains” of PCs, servers, equipment, and automatic systems.
In addition, the practical implementation of ANN is used in other areas of AI modeling, solving mathematical problems, etc.
How a neural network works.
As already noted, they are mathematical models based on biological organisms. Depending on the area in which the neural network is used, it can have different interpretations that will be correct in this particular case. For example, if we consider the field where a computer learns independently, following given algorithms, ANN acts as a set of tools for pattern recognition, and in the field of creating and studying the principles of operation of self-driving machines – a model for controlling “electronic brains”.
In addition, the neural network is a key factor in the development of artificial intelligence. One of the main advantages that distinguishes this system from traditional computational algorithms is the possibility of self-learning with subsequent independent reproduction of the acquired knowledge and skills in practice. From a scientific point of view, the learning process here is rather primitive and in the future it will develop according to a more complex scenario.
The brain of an ordinary person, on average, consists of eighty-six billion neurons, which are connected into a single system for the purpose of receiving information, processing and further transmitting it. Here, each biological neuron is something like a device that is responsible for performing arithmetic, logical and control operations.
ANN is an imitation of the processes occurring in the brain of an average person: collection, processing and transmission of data occurs according to the same principles. And the main difference is that the control process is written in machine code. The following groups are responsible for the process of the neural network:
- Entry points. These are special holes through which data flows. Their number can reach a huge number.
- Exit points. Unlike the previous group, an individual neuron can have only one exit point, through which the processed result is issued.
- Intermediate associative neurons. They are located between the two previous groups and represent the main toolkit for processing incoming information.
A neural network is needed in order to solve complex problems that ordinary computing systems cannot cope with, that is, those that require the “plastic” of the human brain for analytical work. In the modern world, ANN is widely used in business, programming, and the Internet. Among the most common uses are:
- This method is the division of data into varieties according to some important characteristics and parameters. For example, in the banking sector, this is used to make a decision on the issuance of loans: the system approves a loan for one person, and refuses to another. In large financial and credit organizations, this work is performed by a neural network, analyzing data such as the client’s age, solvency, credit history, etc.
- Forecast of events. It uses the technology of analytics and predicting the next logical step or event. For example, the outcome of economic situations in the stock market, the rise or fall of stocks.
- This is one of the most popular applications. Today it is widely used in Google. For example, when the user is looking for a photo, or when focusing, when a person’s face is recognized and highlighted.
These are the main directions of using ANN. Of course, the list does not end there – they are widely used in cybernetics, programming, architecture and other areas of human activity.
A kind of ANN.
To give the most complete answer to the question of what a neural network is, how it works and what tasks it performs, it is necessary to determine its types. Convolutional and recurrent neural networks are distinguished.
The first type – convolutional – is the most popular type. Their effectiveness has been tested in practice. First, for the recognition of visual images: be it a picture, photograph or video. Secondly, to predict any objects that will be of interest to the user on the Internet (video, music, books, etc.). In this case, they are used to collect and process certain information about a person, taken from open sources on the Internet, and the history of search engine queries.
Recurrent species in the process of work, that is, the connections of individual neurons, form a simple cycle. Moreover, each connection has individual characteristics. For example, they differ from each other in priority, and their nodes can be hidden and introductory. Unlike the previous type, here you can give the machine instructions on which objects or data to “pay attention”, and subject it to more careful processing. They are used, as a rule, in text recognition, be it a Google translator, or a built-in application in smartphones with the iOS operating system – Siri.
How the neural network is trained.
One of the main functions of neural networks is the ability to increase your own knowledge base of skills based on previously obtained information. Productivity increases with learning. The learning process here is possible due to the synaptic weights of interneuronal connections, as well as the thresholds and parameters of their activation functions.
The very concept of “learning neural networks” is associated with a huge number of different processes, which makes it almost impossible to define the process itself. But the generalized thesis can be formulated as a process in which the unoccupied parameters of the ANN are automatically adjusted to the environment in which the network is embedded. Thus, the type of training depends on the settings.
