Disclaimer: I am a researcher myself, probably very biased by now, and don’t know the latest version of Tensorflow.
I actually first tried Tensorflow when I (had to) get started with Deep Learning. I’m not a hardcore DL/ML/AI researcher, but use it in a more applied fashion mainly in the context if NLP. However, once I tried PyTorch, I quickly switched and had so far no reason to look back. Two simple reasons, obviously very subjective:


I find the usability/readability of PyTorch much more intuitive. It just writes more like normal Python/numpy compared to the Tensorflows session concept. Sometimes I still have to look at Tensorflow code – for example, when I’m looking for an implementation of an architecture I’ve found in an paper – and the code just looks more “bloated”.


At least back then (I’m not very up to date), Tensorflow might have had the two advantages of having a better performance and be able to deplyed across different platforms including mobile devices. But for me as a researcher, these were/are non-issues.


In short, In need to implement and test networks quickly. Hence, I’m absolutely on team PyTorch. But then again, some colleagues are happy with Tensorflow, and I’m not trying to convert them :).
As @ptrblck mentioned, just go through some tutorials for both frameworks (and maybe others?), and see what feels “right” for you. I’d just argue that for research readability/usability beats things like (slightly) better performance etc. any day of the week.