Fundamentals of systems learning development for software applications

The work cycle of software training for software development or solving business problems includes several basic stages: developing a platform and a module, creating software code, testing the system in working conditions, and implementing it into a real environment.

A ready-made software learning model can function as an independent piece of software or be part of a large and complex application for organizing and processing big data. Our ml development services will help you achieve your goals.

Statement of the problem and purpose

Any software is created to solve specific business problems. Initially, there should be a clearly articulated goal to be achieved. It is important that the task and goal are absolutely achievable. The development stages and timing should take into account the current situation and possible changes in the software environment at the time of the release of the application. That is, by the time of readiness, the developed software must be integrated into all updates and versions of standard software shells.

Programmed learning algorithms form the backbone of many data processing and analysis applications. This technology allows you to automate processes, extract the necessary data from a large flow of information and significantly improve the efficiency of software.

The traditional formulation of a problem for a programmed learning system includes setting a goal, selecting action algorithms, developing program codes and archiving into a ready-made solution format with subsequent integration into any environment.

For example, for an application for recognizing handwritten numbers, the programmed learning algorithm has shown high accuracy and consistency in solving specific information reproduction and translating handwritten characters into digital format.

If the algorithm of training actions includes a small number of functional formulations, then the recognition result worsens and the task becomes more complicated. Accuracy and efficiency are increased with a rich set of training actions, and the application can independently translate characters into numeric values ​​without much operator intervention.

 If action templates are not sufficient for complex tasks, you can expand existing modules by adding new action parameters. By artificially expanding the dataset, you can increase the efficiency of your application by up to 20%.

Receiving and processing data

It is imperative to define a source dataset that will be used as parameters for programming code development. In most cases, programmed learning algorithms provide the best performance when using large sets of training algorithms. The more data is included in the algorithm, the more accurately and better the system will perform the assigned tasks.

As a result, data collection is a key argument for successful training code. In conditions of inaccessibility of certain information or a shortage of the necessary equipment for collecting and organizing data, you can use template solutions that will only need to be optimized for your task and software environment.

The public database is large enough. Sometimes, after defining the goal, developers find that this task has already been solved and that ready-made open-access code can be borrowed.

After systematizing the data, they should be studied and the algorithm of actions should be simulated in advance. To get a working code, you need to define all the functions of the algorithm that will affect the final result. For example, to determine the height and weight of a user based on primary data from the customers themselves, the age factor was not taken into account. Older people are more likely to be obese, and a system with a learning algorithm for computing simply ignored this parameter and often made a mistake with the forecast.

Fragments of “hidden parameters” can sometimes lead to fatal errors. This is especially true for routing systems, weather sensors, life support systems and medical equipment. In real-life situations, the algorithm must be tested in all life cycles of the application and receive the approval of the specialized specialist. An attempt to develop a programming model with a limited complex of computations leads to ineffective applications with a low coefficient of accuracy and performance.

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