Право и управление2224-913310.24412/2224-9133-2026-5-412-422OtherПРОГРАММНОЕ ОБЕСПЕЧЕНИЕ ДЛЯ МАШИННОГО ОБУЧЕНИЯ: АРХИТЕКТУРА, ФОРМАТЫ И ПРОБЛЕМЫ СОВМЕСТИМОСТИMACHINE LEARNING SOFTWARE: ARCHITECTURE, FORMATS AND COMPATIBILITY ISSUESСеменовАлександр АлександровичSemenovAlexander Alexandrovichmail@law-books.ruГончарВладимир ВладимировичGoncharVladimir Vladimirovichvg0778@bk.ruChief Researcher, Federal State Budgetary Institution “STIS”, of the Ministry of Internal Affairs of Russiaнаучный сотрудник ФКУ НПО «СТиС» МВД РоссииAssociate Professor, Department of Information, Technology and Cybercrime Investigation Moscow Academy, of the Investigative Committee of the Russian Federation, named after A. Ya. SukharevДоцент кафедры информационных технологий и, организации расследования киберпреступлений, Московская академия Следственного комитета, Российской Федерации имени А.Я. Сухарева0101202654124222805202619062026

В статье рассматриваются особенности программного обеспечения для машинного обучения, включая фреймворки и библиотеки. Анализируются основные проблемы совместимости форматов данных, описания нейросетей и разметки датасетов. Описывается внутренняя структура фреймворков, их взаимодействие с библиотеками и особенности работы с различными форматами данных. Особое внимание уделяется проблемам совместимости при описании архитектур нейросетей и преобразовании данных

This article examines the features of machine learning software, including frameworks and libraries. Itanalyzes the main issues of data format compatibility, neural network description, and dataset labeling. Itdescribes the internal structure of frameworks, their interaction with libraries, and the specifics of working with various data formats. Particular attention is paid to compatibility issues when describing neural network architectures and data transformations

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