COMPARATIVE ANALYSIS OF MACHINE LEARNING METHODOLOGIES AND TECHNOLOGIES

Mukhitdinova, Munavvarkhon

Рақамли иқтисодиёт ва ахборот технологиялари · 2025-yil

Annotatsiya

This paper presents a comparative analysis of three key ML paradigms—supervised, unsupervised, and reinforcement learning—alongside an evaluation of popular ML frameworks such as TensorFlow, PyTorch, and Scikit-learn. The study explores the key differences, advantages, and limitations of these approaches, focusing on factors like computational efficiency, scalability, and ease of implementation. The findings provide valuable insights into how different ML methodologies and technologies shape real-world applications and influence practical decision-making in AI-driven systems.

Maqola ma’lumotlari
MualliflarMukhitdinova, Munavvarkhon
JurnalРақамли иқтисодиёт ва ахборот технологиялари
Nashr sanasi2025-06-26
Jild5
Son2
Betlar149-156
TilIngliz

Kalit so‘zlar

machine learning, supervised learning, unsupervised learning, reinforcement learning, deep learning, ML frameworks, TensorFlow, PyTorch

Ilmiy soha

Рақамли иқтисодиёт ва ахборот технологиялари jurnalidan boshqa maqolalar

Рақамли иқтисодиёт ва ахборот технологиялари — barcha maqolalar