Price: 0
Number of applications: 1
10.06.25 (inclusive)
Open source project / opensource
MVP
ICT tasks
Media sphere
Neurotechnology and artificial Intelligence
Purpose and description of task (project)
The purpose of the project: Create an intuitive educational platform that allows users to train machine learning models directly in the browser with visual visualization of the process — for a deep understanding of key ML concepts without the need to install software. Project Description: The project is an interactive web application in which users can train simple machine learning models (linear regression, neural networks, etc.) in real time, observing changes in weights, errors, and results on visual visualizations. It is based on the use of client technologies (for example, TensorFlow.js), which allows you to run training directly in the browser — without server load and installation of additional libraries. The application will include: Selecting and configuring models; Educational tips and theory; Visualization of learning (loss, weights, predictions); Working with embedded and custom datasets. The target audience is novice machine learning specialists, students, teachers, as well as anyone who wants to understand how AI algorithms work through interactive practice.
Software/ IS,
Mobile app
Problem description Learning machine learning (ML) often starts with theory, formulas, and complex frameworks, which makes entering the field difficult and demotivating for beginners. Most of the training resources require the installation of specialized software (Python, ML libraries, Jupyter, etc.), a powerful computer, and technical training. As a result: Students lose motivation due to the difficulty of launching the environment and the lack of visibility. It is difficult for teachers to explain abstract concepts such as gradient descent, activation functions, and retraining. Visual and interactive understanding of the models' internal processes remains unavailable. Thus, there is a need for an accessible, visual and interactive environment that would make it easy to train and explore the behavior of ML models without technical barriers - right in the browser and with visual visualization of all key processes.
Expected effect Reducing the barrier to entry into machine learning Thanks to the ability to train models directly in the browser, users will be able to start practicing without installing software, configuring environments, or learning the command line. Improving the effectiveness of training Visual visualization of weights, errors, and the learning process contributes to a deeper understanding of the internal mechanisms of ML models. Unification and standardization of the educational process The platform can be used in schools, colleges and universities as a single tool for teaching the basics of artificial intelligence. The growing interest in the field of AI among young people The interactive and "live" format increases engagement and motivation to study Data Science and related technologies. Potential for scaling and commercialization In the future, the project can be expanded to a full-fledged educational ecosystem integrated into online courses, EdTech platforms and school curricula.
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