Which is better for deployment: PyTorch or TensorFlow?
Both frameworks have their strengths. TensorFlow is often preferred for production due to its robust deployment options, while PyTorch is favored for research and prototyping.
What are the specific deployment tools available for each?
TensorFlow offers TensorFlow Serving and TensorFlow Lite for mobile, while PyTorch has TorchServe and ONNX for interoperability.
How do they compare in terms of ease of use?
PyTorch is generally considered more user-friendly and intuitive, especially for beginners, while TensorFlow has a steeper learning curve but offers more features.
Can you give examples of companies using each framework?
Sure! Facebook uses PyTorch for its AI research, while Google heavily utilizes TensorFlow for various applications, including Google Photos.
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