TensorFlow vs PyTorch: The Real Difference Isn't Accuracy

Short summary

Hands-on comparison of TensorFlow (Keras API) vs PyTorch on CIFAR-10 image classification. TensorFlow offers concise, beginner-friendly code with automated training loops; PyTorch requires explicit model classes and manual training loops, providing more control for customization and debugging. Framework choice depends on whether you prioritize rapid development or research flexibility.

• •TensorFlow provides compact Sequential API ideal for rapid development and production workflows
• •PyTorch uses explicit class-based models enabling greater flexibility and easier debugging
• •Same CNN architecture on identical CIFAR-10 setup demonstrates implementation differences, not accuracy differences

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