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.

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.

I'm know the TensorFlow API really well considering I've been using it for about 2 years now.

I've used PyTorch before as well, its really not hard to learn the other once you're good at one of them.

PyTorch is a little more verbose than TensorFlow , e.g. TensorFlow has a default training loop that can be started by invoking the fit method where as in PyTorch you write the training loop yourself regardless, you always have to explicitly call methods for AD/Back propagation as well in PyTorch.

One of the posters in that thread says "Google always struggled to build a long lasting ecosystems for their products", I don't even see how that is relevant anymore since TF has been open sourced and is not necessarily maintained by Google anymore.

PyTorch is more popular I concede that, but I don't think the people responsible for maintaining TensorFlow are going to shut up shop just because PyTorch is modestly more popular.

I don't think one guy saying "TensorFlow is on its way out" constitutes it reaching EOL lol

Um, I don't think TF2 is reaching EOL anytime soon? Do you have any other sources besides a blog post regarding this?

Try both frameworks and pick the one that is more intuitive for you, you can always circle back and learn the other if you need anyway.

Transformers do generally need to be pre-trained on a large corpus to do well on further downstream tasks.

From my perspective, R is great for certain tasks (more related to classical machine learning and statistical analysis as you have mentioned). There are several libraries that have demonstrably better implementations in R than Python (lme4 comes to mind).

I will have to circle back to the same point regarding deep learning and Python. Whilst R does offer deep learning capabilities in its modern iterations, the uptake of using R in such a fashion pales in comparison to the uptake of Python, so the same comment I made about MATLAB still applies.

TensorFlow/Keras and PyTorch are the gold standard for deep learning frameworks within the industry, and these are both Python frameworks. It's not just about it being open source, its more about it being the tool of the trade. Nobody is going to employ you within a team that uses Python frameworks for deep learning, unless you are skilled at those frameworks.

MATLAB isn't really popular for deep learning, even though it does facilitate some deep learning capabilities (relative to Python).