TensorFlow vs PyTorch: Two Paths to Deep Learning TensorFlow is built with structure, performance, and large-scale deployment in mind. It emphasizes optimized computation and production-ready workflows, making it a strong choice for building and serving machine learning models in real-world systems where efficiency and scalability matter most. PyTorch, on the other hand, prioritizes flexibility and ease of experimentation. Its dynamic approach allows developers to build, test, and modify models

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