COMPARATIVE ANALYSIS OF MACHINE LEARNING METHODOLOGIES AND TECHNOLOGIES
Ключевые слова:
machine learning, supervised learning, unsupervised learning, reinforcement learning, deep learning, ML frameworks, TensorFlow, PyTorchАннотация
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.
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