Development Philosophy TensorFlow takes a production-first approach, emphasizing scalability, deployment, and enterprise features. Originally built around static computational graphs, though TensorFlow 2.0 introduced eager execution by default. PyTorch prioritizes research flexibility and intuitive development. Built from the ground up with dynamic computational graphs and a “Pythonic” design philosophy that feels natural to Python developers. EaseContinue reading “TensorFlow vs. PyTorch”
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Notes from San Francisco 