I’ve used TensorFlow since 2016 but I grew tired of it, it’s not really open source, it’s just Google sharing their tool, so I switched to PyTorch as they do iterate faster and care more about the community

TensorFlow versus PyTorch is a matter of taste imo, I just like TensorFlow because I find it more intuitive than PyTorch but everyone is different.

That but PyTorch has also matured a lot, we tried to switch in 2018 and deploy the new shining model to production but back then PyTorch had terrible performance, now that’s not the case, it had matured and I think will go farther on the shoulders of the community.

That but PyTorch has also matured a lot, we tried to switch in 2018 and deploy the new shining model to production but back then PyTorch had terrible performance, now that’s not the case, it had matured and I think will go farther on the shoulders of the community.

PyTorch is great, its honestly much easier to use than TensorFlow, especially for beginners. TensorFlow however offers everything PyTorch does through heavy use of object oriented design (primarily inheritance).

The functional model in TensorFlow is very similar to the default way of instantiating models in PyTorch. TensorFlow has both many many convenience wrappers but also gives you the full freedom that PyTorch does, given that you are able to deal with the nuances and complexities of object-oriented design and refer heavily to the documentation.

The thing with me is that I started with TensorFlow v1 back when PyTorch wasn’t even in the race, and because of the constant breaking changes to the TensorFlow API and cryptic error messages, my experience was hellish TBH. Even getting support from stackoverflow was messed up because people would be posting solutions for different API versions. Then PyTorch got released and boy was it the savior I needed. It literally saved me hundreds of hours of debugging (and possibly from brain hemorrhage too). Compared to the burning hell TF1 was, PT was like coding on a serene beach. And then TensorFlow v2 came out with eager execution, that promised PyTorch way of doing things. But then the question is, why switch if it is the same as PyTorch? And so I didn’t.

I’m coming from a research point of view. If I was coming from a production POV, things could’ve been different.

Which one would you recommend a beginner to put time in to learn? Which one is more practical and worth putting time in?

And which one is more frequently used in the market?

For an absolute beginner, definitely PyTorch is what I would recommend. It’s like an extension of numpy.

Both frameworks are extremely matured and will get the job done no matter what you throw at it (I don’t get what you mean by practicality).

For industry purposes, if you have a particular company in mind, then check which framework they use (ask some employee on LinkedIn) and learn that framework (some companies still have their codebases in TF1, they never updated). If you are in the market for a job hunt, then having both on your CV will give you the best chance.