In recent years neural network training becoming more popular. It is used in various fields of activity: engineering, medicine, physics, engineering, business, geology. Why has the neural network become so popular ? This is because the work and training of a neural network involve solving practical problems that it successfully copes with.
Reasons for popularity
Experts explain the success of introducing neural networks into practice for several reasons:
- rich opportunities;
- ease of use;
- attractiveness.
We will dwell on each item in more detail.
Teaching a neural network with a teacher is a powerful modeling method that allows you to consider the most complex dependencies.
Learning neural networks with examples. The user needs to select representative data, then run a learning algorithm that automatically perceives the structure of the entered data.
It will take some initial set of heuristic knowledge about the selection and preparation of data, the selection of the necessary network architecture, and interpretation of the results. Only then multilayer neural network training will be successful. But this level is much simpler than using classical statistical techniques.
The training of the convolutional neural network attracts users, as they are based on a simple biological model of the nervous systems. Improving such neurobiological models will lead to the creation of unique thinking computers.
Scope of application
Neural network training allows you to recognize texts, speech, carry out semantic search. Among the areas of their application, we highlight systems that help make decisions, analyze stock prices, texts, and control the security of the World Wide Web.
Features of the educational process
Before we talk about how the neural network is trained, let us dwell on their features. Artificial neural networks, similar to biological ones, are a computer system with a large-scale number of simple processors operating in parallel with a large number of connections.
Unlike biological counterparts, neural artificial networks exhibit many qualities that are inherent in the brain: generalization, analysis, selection of data from the flow of information.
They can change their behavior depending on the external environment. After analyzing the initial data, they independently configure and learn, providing the correct reaction.
The resulting network is resistant to some deviations of the original data, so there is no distortion due to external interference.
In the middle of the last century, a group of researchers synthesized physiological and biological approaches, created the first artificial neural system.
Without training, it was difficult to fully understand the structure, properties and purpose of networks. It would seem that we managed to find the key to artificial intelligence. But the illusions of man were dispelled quickly enough. Networks easily coped with solving some problems, analyzed data. But they could not cope with other tasks, that is, they were very limited in use.
That is why the training of the neural network was continued, the formation of the scientific foundation for such activities.
At the end of the twentieth century, firms were opened that were engaged in the creation of application software for creating artificial networks. It was at this time that machine learning appeared. Neural networks have proven their effectiveness in solving complex problems, for example, they are used to check the solvency of bank customers.
Teaching methods
In order for the network to solve the tasks assigned to it, it is necessary to train it. This ability is considered to be the main property of the brain. What kind neural network training methods are most effective? By the training process for such systems is meant the process of setting up the structure of connections between individual neurons and synoptic connections, which affect the signals of the coefficients. Created complexes allow you to effectively solve the task of the network. Primarily neural network training happens on some sample. How did you solve this problem? Special neural network learning algorithms . They allow you to increase the efficiency of the reaction to incoming signals, expand the scope of their application.
Learning Paradigms
Deep learning of neural networks is carried out on the following paradigms:
- with a teacher;
- without a mentor;
- mixed form.
The first of them is characterized by well-known correct answers to each input variant, the weights are adjusted so as to minimize the possibility of errors.
Self-training makes it possible to categorize the source samples, this is achieved by disclosing the nature of the data and the internal structure.
The mixed view is considered as a synthesis of the two previous approaches. To train a neural network is to tell it the information that we want to receive from it. This process is similar to teaching a child the ABC. They show him the letter, and then they ask: "What is this letter?" If the answer is incorrect, the child is again explained how to.
The process is repeated until the correct information remains in his memory. This procedure is called βteacher training.β
Process essence
Let's see how artificial neural networks function. Their training is carried out according to a similar scheme. Initially, a specific database is taken containing some examples (a collection of images of letters).
If you show the letter βAβ at the input of a neural network, it gives a definite answer, which may be incorrect. In the form of the desired output in the problem of the proposed classification, use the set (1,0,0, ...), in which the output with the label "A" contains 1, and at all other outputs - the label 0.
When determining the difference between the real and the desired network response, we get 33 numbers - this is the vector of a possible error. You can repeatedly show her the same letter. Therefore, the learning process is considered as repeated repetition of the same exercises (training), therefore, we can say that a fairly deep learning is carried out.
A neural network without training is not ready for operation. Only after repeated demonstration of examples of knowledge will knowledge gradually stabilize, do the systems give the correct answers to the questions asked.
In such situations, they say that deep training has been carried out. Neural networks gradually reduce the magnitude of the error. When its value is reduced to zero, training is suspended. The formed neural network is considered suitable for use on new source data.
Information about the task that the network has is in a set of examples. That is why the effectiveness of training a neural network is associated with the number of examples contained in the training complex. There is also a dependence on the completeness of the description of the problem.
For example, the neural system cannot predict the financial crisis if no scenarios were presented in the training set. Professionals argue that for a quality network training you need to demonstrate at least a dozen examples to it.
The learning process is knowledge-intensive and complex. After its completion, you can use the network for practical purposes.
The main feature of the human brain is the reproduction of acquired information in those situations when it is necessary. A trained network has a large amount of information, which allows you to get the right answer for new images.
To design the learning process, you need to have an idea of ββthe model of the external environment in which the neural network operates.
A similar model defines the task of learning. You also need to understand how you can modify the basic parameters of the network, how to use the settings. The essence of training involves a procedure in which training rules are applied to debug algorithms.
Learning Algorithms for Neural Networks
Currently, several of their options are used:
- conjugate gradients;
- backpropagation;
- Quasi-Newtonian;
- pseudo inverse;
- Kohonen training;
- Levenberg-Markard;
- vector quantizer;
- K-nearest neighbor method (KNN)
- setting explicit deviations.
This is far from all neural network learning algorithms currently applied.
After the number of layers and the number of elements in each of them is revealed, it is necessary to determine the indicators for this network, which would minimize the forecast error that it proposes.
This process can be considered as a fit of the model implemented by the network to the presented training information.
Important points
The error for a specific network configuration is calculated by fitting all existing observations through it and comparing it with the target values ββof the output values.
It is better to use those algorithms that make it possible to train a neural network in a minimum number of steps. They assume a small number of variables. The reason for this choice is that now neural network training carried out on computers that have poor performance, limited memory.
Varieties
Stochastic methods involve a significant number of steps in the learning process. That is why they are almost impossible to use for modern neural networks of large dimensions.
The exponential increase in search accuracy with an increase in the task dimension dimension optimization algorithms does not allow the use of such systems in the learning process.
The conjugate gradient method is highly sensitive to the accuracy of the calculations. In particular, when solving optimization tasks of a large-scale regularity. They need to use additional variables.
All the neural training algorithms currently used are based on the evaluation function. This allows you to give an overall assessment of the quality of the entire network.
They are considered quite simple, therefore they do not provide a good control system in a short time, and are not suitable for the analysis of complex systems.
Learning Acceleration Options
Since neural networks are considered one of the manifestations of artificial intelligence, they are often used in pattern recognition, solving optimization problems.
Many models of such networks have been created that cope with a variety of application tasks. Each of them has its own algorithms and training methods. Despite this diversity, work on improving the algorithms and creating new models does not stop, but the theory of networks itself is not yet sufficiently formalized.
Development stages
There are two main stages that are used in the development of neural networks. Structural synthesis involves the selection of a specific model, as well as an analysis of the preliminary structure, learning algorithm.
Parametric synthesis includes more than just process neural network training , but also a quality check of the results. With this in mind, you can decide to return to the initial stages of a parametric or structural analysis.
The incomplete formation of the stages leads to many problems in the created network. For example, at the stage of structural synthesis during the selection of a model, structure, algorithm, great efforts, the help of experienced computer developers will be required.
At the stage of parametric synthesis during training, limited computing resources arise. Tasks with a complex structure will require great efforts from neural systems, so the process involves significant time costs.
There are certain techniques to reduce such costs by learning neural multilayer networks . They are based on the principle of sufficiency, in which the system error cannot exceed a certain indicator. For example, such methods include the correction of the steps of modernization of weighting coefficients, the conversion of recognized classes.
Is produced neural network training until its error reaches zero. This is due to the large expenditure of time resources, because it is not immediately possible to detect an error, to eliminate the cause of its occurrence.
Conclusion
Determine Performance neural network training You can, using a specific task, the desired result.
For example, if a specific task related to classification is proposed, then a multilayer neural network will be required to solve it. For its training, a modern algorithm for the back propagation of errors is suitable.
Evaluation of the possible error that occurs during the learning process is carried out in two ways: global and local. The second option assumes the presence of neuron errors in the output layer. For the global view, the presence of the entire network on the ith training set of errors is assumed.
Training can be considered ideal if, after it, the network repeats the training sample in full, does not give errors and malfunctions.
Such training is labor intensive. It is achieved only in rare cases. The principle of sufficiency consists in the complete rejection of the search for the ideal in the performance of a specific task. If you transfer it to the training procedure of a modern neural network, then ideal accuracy is far from always observed.
To recognize an object, as well as its class, features, it is allowed that the network error in the set does not exceed the indicator Ξ΄. Such a value will be considered the maximum indicator at which the accuracy of the calculations is maintained.
The neural network approach is particularly effective in fulfilling tasks related to expert assessment and processing of information of various kinds.