A Comparative Survey of PyTorch vs TensorFlow for Deep Learning: Usability, Performance, and Deployment Trade-offs
Descripción general
Resumen del artículo
This paper presents a comparative analysis of two prominent deep learning frameworks, PyTorch and TensorFlow, exploring their usability, performance, and deployment aspects. It finds that PyTorch prioritizes ease of use and dynamic model building while TensorFlow excels in production deployment and ecosystem support. The survey suggests choosing a framework based on project-specific needs, acknowledging strengths in both.
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PyTorch and TensorFlow are popular tools for building AI. PyTorch is easier to learn and more flexible, while TensorFlow is better for deploying finished AI models.
Posibles conflictos de intereses
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Limitaciones identificadas
Explicación de la calificación
This survey offers a comprehensive comparison of PyTorch and TensorFlow, covering key aspects relevant to developers. While it doesn't present new experimental findings, its synthesis of information from various sources provides valuable insights. The lack of original research and focus on only two frameworks are minor limitations, but the overall quality and depth make it a strong resource.
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