Another key difference is the programming paradigm. TensorFlow follows a more declarative programming approach, where you define the model structure and then let the library handle the execution. In contrast, PyTorch embraces an imperative programming style, allowing you to write code in a more imperative and iterative manner. This can be advantageous for debugging and prototyping complex models quickly.

Regarding performance, both TensorFlow and PyTorch have comparable performance for most deep learning tasks. However, TensorFlow has long been favored in production environments due to its extensive deployment options and pre-trained models availability. PyTorch, on the other hand, has gained popularity for its ease of use, dynamic nature, and strong support within the research community.

One major difference between TensorFlow and PyTorch lies in their computational graph models. TensorFlow uses a static computational graph, which means the graph is defined and compiled upfront before the execution. On the other hand, PyTorch uses a dynamic computational graph, allowing flexibility and ease of debugging. This dynamic nature of PyTorch makes it more Pythonic and intuitive. However, the static computational graph in TensorFlow enables optimizations and potential performance gains, especially for large-scale distributed training.