Which framework should I choose: TensorFlow or PyTorch?

I'm trying to decide between TensorFlow and PyTorch for my deep learning project. Which one should I choose?

Both TensorFlow and PyTorch are excellent frameworks, but they have different strengths. TensorFlow is great for production and scalability, while PyTorch is more user-friendly and flexible for research.

What are the main differences in terms of ease of use?

PyTorch is often considered easier to learn and use due to its dynamic computation graph, which allows for more intuitive coding. TensorFlow, especially with its Keras API, has improved in usability but can still be more complex.

How about community support and resources?

Both frameworks have strong community support. TensorFlow has a larger ecosystem and more resources for deployment, while PyTorch has gained popularity in academia, leading to a wealth of research papers and tutorials.

Can I switch from one to the other later on?

Yes, you can switch between TensorFlow and PyTorch, but it may require some effort to adapt your code and models. It's a good idea to choose the one that aligns best with your current needs.