Many modern technical developments are based on the principles of artificial intelligence and neural networks.

The neural network works on a human-like algorithm, using artificial neurons and synapses.

Neural networks are responsible for solving complex logical and mathematical problems. They are ideal for the classification and analysis of information, forecasting and probabilistic events.

Also, networks are well cope with the recognition of certain information. Each neuron in the network is an independent computing unit and a transmitter conductor. The whole network has an input layer that receives a certain set of data and output – outputs the result. Synapses are the connecting links of the neurons and form the basis of the neural network.

The system is capable of developing at a rapid pace. The basis of the training consists of two types of information: the transfer of data by the mentor (downloading the necessary data) and private training. The effectiveness of both methods is impressive. The machine can show the high accuracy of forming answers, different solutions and processes.

Speech Recognition Accuracy 97%
Until recently, various systems for translating languages, speech recognition, individuals and images required the availability of productive data centers for data processing and information extraction. Corporations like Google and Microsoft have already created their own neural applications that can work autonomously on users’ devices without having to connect to servers. Special software can be downloaded to your mobile phone or computer.

But recently, a group of scientists from the University of Waterloo developed a system for recognizing the language. This unique software product called EdgeSpeechNets. The software runs on a neural network and is used to recognize the language. The creators claim that the program is silent to iron and shows a significant 97% accuracy of recognition, even on older smartphones. The process of recognition captures even the quick words and accents with intonations. This approach can be used in synchronous translation, digitalization of the language and reading of information from different carriers.

Neurons in online casinos and bets
Gembling on the Internet is also considered one of the interesting and perspective niches for the implementation of neural networks. If you get acquainted with online casinos that are available on the territory of Ukraine for playing hryvnia, you can notice that many of them use self-learning systems and implement them in the form of bots, etc. For example, one of the flagging events that showed the effectiveness of the machine, which itself was the DeepStack algorithm that beat the Texas Hold’em players. Within twenty days, the machine was played by four professionals in card art with a total score of 120,000 hands. To launch the program is enough power of the average laptop or even a smartphone.

Sports betting also became part of the test for neural networks. At the University of Lausanne, a network has been created that analyzes football teams, their match statistics, the individual performance of each team member and many other options. When drawing up the coefficients for the match, the bookmakers are guided by the herd instinct of the crowd and a certain set of clichés. The program uses more accurate calculations. As a result, the network predicted the exact result in 80% of cases.
Neural networks are also beginning to be used when testing gaming machines and emulators. The system collects statistics and analyzes the set of indicators. Based on the data obtained, the program allows you to predict one or another result with high accuracy. The revolutionary approach to memorizing and manipulating a large amount of information contributes to the use of the neural network in many games and forecasts.

Artificial neural networks in computer security
Technologies are developing so fast that many ordinary citizens do not have time to keep up with the news. Recently, neural networks are actively used in computer security. Thanks to quick self-learning and data loading capabilities, the system can analyze input information and identify deviations from the norm. The algorithm for recognizing individuals works on the same principle. The system builds a model of a person, its mimicry and features. Creates a grid of distinctive rice that is remembered by the program. Thus, software or various objects are under secure, round-the-clock protection.

The algorithms are capable of self-learning and the detection of previously hidden security vulnerabilities. The security system can adapt to constant changes and block access to data from third-party requests or when breaking. The collection and analysis of biometric data is one of the leading directions of modern neural networks. The system can redirect malicious inquiries by protecting the main databases by protecting the server from DDoS attacks. Third-party users will not be able to access important parts of the hardware or software.

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