TensorFlow and PyTorch are leading frameworks for deep learning, each with distinct strengths. TensorFlow excels in production environments due to its comprehensive ecosystem, including TensorFlow Lite for mobile and TensorFlow Serving for deployment.
PyTorch is favored for research and experimentation, offering a dynamic computation graph that makes debugging and prototyping intuitive. PyTorch’s popularity among academics has driven innovation, resulting in a vast collection of community-contributed models and tools.
Both frameworks support distributed training, hardware acceleration, and integration with tools like Keras (TensorFlow) or Hugging Face (PyTorch). The choice often depends on the project’s requirements and the team’s familiarity with the framework.