VISUAL SYSTEM FOR SETTING UP MACHINE LEARNING ALGORITHMS AND DATA

Abstract

The article presents the development of a visual system for designing and configuring machine learning algorithms and datasets, aimed at reducing the complexity of building deep learning models. Despite the rapid growth of artificial intelligence and neural networks in recent decades, the creation and configuration of models have remained accessible only to specialists with strong programming skills. This work addresses the gap by developing Learn2Learn, an open-source software tool with a graphical interface that allows users to construct, customize, and train deep learning models almost entirely without coding.

The study emphasizes the practical relevance of integrating graphical user interfaces (GUIs) into machine learning workflows, enabling a broader range of users, including researchers, educators, and beginners, to interact with neural networks more intuitively. The system is structured around key stages of machine learning: data loading and preprocessing, model construction, selection of loss functions and optimization algorithms, and monitoring of training progress through visualized metrics. Unlike most existing tools, Learn2Learn supports the integration of custom dataloaders and model layers, ensuring flexibility comparable to traditional coding while significantly lowering the entry threshold.

The article provides an overview of implemented functionalities: visual model construction using drag-and-drop neural network layers, interactive parameter adjustment, real-time error visualization, and integrated recommendations for model design. The program supports diverse data types, including images, text, and numerical values, and allows preprocessing through augmentation techniques. By combining PyTorch as the computational backbone with PyQt6 for GUI design, the authors demonstrate how the system maintains both usability and technical rigor.

A comparative analysis with existing visual tools highlights the advantages of Learn2Learn in flexibility, expandability, and error handling. In particular, user-friendly error messages and interactive hints help prevent common mistakes, making the system not only a development tool but also an educational platform. The authors emphasize that Learn2Learn is still at a prototyping stage, with future improvements planned, such as integration of standard datasets, pre-trained models, and distributed training on remote servers.

The article concludes that the developed system significantly reduces barriers to entry into deep learning by providing an accessible, extensible environment for model creation. The prototype illustrates the feasibility of unifying the simplicity of visual interfaces with the flexibility of code-based programming, opening prospects for both educational applications and practical research in artificial intelligence.