I guess it depends a lot on perspective, to me the whole field looks like alchemy more than science. The results cannot be denied, but we understand so little.
Now you have the likes of HuggingFace which is another abstraction on top of Pytorch. Heck you don't even need to code a transformer from scratch. They have done it for you.
from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") model = AutoModelForMaskedLM.from_pretrained("bert-base-uncased")
I was like wow, this level of abstraction is totally breaking barriers to entry and we will see many many flavours of huggingface in the years to come.
If you want to do research in any branch of ML, you are almost certainly going to need some strong mathematical foundations. At the same time, if you're more interested in applying existing architectures/models, the ecosystem has a lot of really amazing tools that will take you a very long way without requiring you to study statistical learning theory.