Neural networks are automated structures, which are caused by the example of natural sensory chains that form (set up) the animal. In other words, Neural networks in development are sets of algorithms simulating human brain activity. That sort of structure study to complete the assignments, by looking through the samples, without being planned out.

Neural Networks comprise a series of the united elements or nodes named artificial neurons that easily imitate themselves in the organic substance. Each contact can transfer one signal from one AN to different ones. The imitation neuron that gets the indicators may convert it and gives signs to neurons united with it.

The signal in the contact among neurons may be a real sum. The finished product is generated by the set of the non-continuous functions of some original material. The layers are the basement of the artificial neurons.

Neural Networks Services
Neural Networks development

Goal of Neural Networks

The primary aim of the NN is to settle the problems in the same approaches the personal brain does.

Sensory systems can be applied for:

Computer visionSpeech identificationAutomated translationSocial networks cleaningGamingMedicine identification of diseases

Allocation of the NN

There are many samples of the NNs, every of which requires functional utilization and the levels of complications.

The most used kind of NN is a feedforward NN (one way from the beginning till the end).

The second kind is a recurrent NN (many ways of transitions).

The third NN is convolutional.

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Tasks of the Neural Networks


Neural networks development tools

CUV LibraryMXNetOpenDLElastic thought

Neural networks development tools

The frameworks of the Neural Network are provided below:

CaffeCUV LibraryElastic thoughtMXNetOpenDL

The set of the libraries for the neural networks advancement is the following:


The languages that are accepted for the Neural Network creation are listed:


The Neural Network development tools are suggested in the table:

ConvNet (Matlab toolbox)DeepLearnToolBoxDeepnet (toolkit)Nengo (software)Pdnn (Python toolkit)

Development of neural networks for system identification

It refers to the original neural networks formation, the ability of neural networks, and displays the reasons why neural networks are used in system identification.

The suggested technical method defines that the SI is built by settling the attributes within a chosen standard, while the input conforms with the identified arrangement. Next is the prediction. This is the initial aim of the system identification.

The original goal is to establish the mathematical model of the physical process for the checked info.

Imitation is a substantially significant approach to researching, studying, and realizing the world. In system imitation, 3 principles are applied such as division, choice, and advantage.

Neural networks application spreads on different spheres, being a great solution for any industry.

SI is a principal demand in the following spheres:

  • LAW


What is Neural networks?
➢Neural networks are automated structures, which are created by the example of natural sensory chains that form (set up) the animal brain. That sort of the structures learns to complete the assignments, by looking through the samples, without being directly programmed.
What are the main goals of NN?
➢The primary aim of the Neural Network is to settle the problems in the same approaches the personal brain does.
Where is NN used?
➢Today, neural networks are used for solving many business problems such as sales forecasting, customer research, data validation, and risk management.
Where can I look at your cases?
➢You can look at our cases here.
Neural network technology

Neural network is a joined neuron set that applies estimating kind for info converting. In majority cases NNs are realized to form stable interconnections of inputs and outputs, to discover markers in info.

Neural network technology is a practice of trying to duplicate the actions of the human in a computer. It is created to let computers practice everything humans can accomplish.

Neural network solution is an effective way to find clear answers on such types of spheres where classification, prediction, optimization and pattern recognition are essential.

In some cases our company makes design solutions for all spheres beveraging NN hi-tech to find info and categorize details.

In web development our specialists also set neural networks for more capacity, efficiency. The networks usually are able to convert a large amount of data and create the most comfortable environment for users.

With the improvement of NN and AI, the whole market world promotes brands with new innovative ideas applying NN. Better market competitiveness and audience categorization will help to show the real picture of future clients and their demands.

Our team is constantly ready to improve our skills on AI, practice,produce new products and help customers to enter the market profitably.

NN order consists of 3 layers: the input , the hidden , the output,which combine the ideal and clear NN. Every layer of NN has a special role. Neural network is also named as a multi-layer perceptron. The layers are made of nodes. The purpose of the input stage is to get the info from outside. The hidden stage is responsible for back-end tasks. The output stage transfers are the product of the converting info.

Our studio can provide neural network services for our clients. We guarantee the development of NNs for your product which can be improved and work effectively and without any bugs and errors.

Our neural network company ServReality is a good partner to deal with high-quality products, fast work and constant assistance are our advantages.

Neural networks are the foundation of the conclusions that prognose customer requirements, evaluate product arrival and more essential details.

NN are applied in the set of enterprise apps, covering decision-making, marker identification, symbol, picture, and sequence recognition.

There is no technology without shortcomings. The NN is no exception. The most frequent con of nn hi-tech is the period for net training applying a large volume of data.

Neural networks development takes a significant place within a development.

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