Neural Networks: Applications in the Real World

Neural Networks: Applications in the Real WorldMany modern technical developments are based on the principles of artificial intelligence and neural networks.

The neural network works in a way similar to the human brain, through the use of artificial neurons and synapses.

Neural networks are responsible for solving complex logical and mathematical problems. They are ideal for classifying and analyzing information, making forecasts and probabilistic events.

Networks also do well with recognizing certain information. Each neuron in the network is an independent computing unit and conductor that transmits information. The entire network has an input layer that receives a specific set of data and an output – an outstanding result. Synapses are neuron linkages and form the basis of a neural network.

The system is able to develop at a rapid pace. The basis of the training consists of two types of information presentation: the transfer of data by the tutor (downloading the necessary data) and personal training. The effectiveness of both methods is impressive. The machine can show high accuracy in the formation of answers, various solutions and processes.

Speech recognition with an accuracy of 97%
Until recently, various systems for translating languages, recognizing speech, faces and pictures required the availability of productive data centers for data processing and information output. Corporations like Google and Microsoft have already created their neural applications that can work autonomously on users’ devices without having to connect to servers. Special software can be downloaded on a mobile phone or computer.

But more recently, a group of scientists from the University of Waterloo developed a speech recognition system. This unique software product called EdgeSpeechNets. The software works on the basis of a neural network and is used for speech recognition. The creators claim that the program is undemanding to the hardware and shows an impressive 97% recognition accuracy even on older smartphones. The recognition process catches even quickly spoken words and accents with intonation. Such an approach can be used for simultaneous translation, digitization of speech and reading information from various media.

Neural networks in online casinos and bets
Gambling on the Internet is also considered one of the most interesting and promising niches for the implementation of neural networks. If you familiarize yourself with online casinos, you will notice that many of them use self-learning systems and implement them in the form of bots, etc. For example, one of the most significant events that showed the effectiveness of a self-learning machine was the DeepStack algorithm, which beat professional players in Texas Hold’em. For twenty days, the car beat four card art professionals with a total score of 120 thousand hands. To run the program enough power of an average laptop or even a smartphone.

Sports betting has also become part of the test for neural networks. The University of Lausanne has created a network that analyzes football teams, their match statistics, the individual effectiveness of each team member and many other parameters. In drawing up the coefficients for the match, bookmakers are guided by the herd instinct of the crowd and a certain set of cliches. 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 statistical data and analyzes a variety of indicators. Based on the data, the program allows you to predict a particular outcome with high accuracy. A revolutionary approach to memorizing and operating with a large amount of information facilitates the use of the neural network in many games and predictions.

Artificial neural networks in computer security
Technology is developing so rapidly that many ordinary citizens do not have time to follow the news. Recently, neural networks are actively used in computer security. Due to the rapid self-learning and the ability to upload data, the system can analyze the input information and determine deviations from the norm. The face recognition algorithm works on the same principle. The system builds a model of the person, his facial expressions and features. It creates a grid of distinctive features that are remembered by the program. Thus, software or various objects are under reliable round-the-clock protection.

Algorithms are capable of learning and identifying previously hidden security vulnerabilities. The protection system is able to adapt to permanent changes and block access to data from extraneous requests or when hacked. The collection and analysis of bio metric data is one of the leading directions of modern neural networks. The system can redirect malicious requests, protecting the main database, protecting the server from DoS attacks. Unauthorized users will not be able to access important parts of the equipment or software package.

Picture Credit: geralt

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