TensorFlow (Google) and PyTorch (Meta) are the two leading deep learning frameworks. Both are powerful but differ in philosophy and use cases.
TensorFlow: production-focused, TensorFlow Serving for deployment, TFLite for mobile, TensorFlow.js for browser. Static computation graph (TF2 uses eager mode by default). Broader ecosystem for deployment.
PyTorch: research-focused, dynamic computation graph, intuitive debugging with Python. Preferred in academia. PyTorch Lightning simplifies training. TorchServe for deployment.
TensorFlow has better production tooling: TFX pipelines, TensorFlow Hub for pre-trained models, and extensive mobile/edge support.
PyTorch has better developer experience: dynamic graphs make debugging natural, and the API feels more Pythonic.
For web developers: TensorFlow.js is the practical choice for browser ML. PyTorch Mobile is catching up.
Start with PyTorch for learning (more intuitive), use TensorFlow for production deployment.