Well now you have the benefit of being able to bring in your trained models from TensorFlow/PyTorch straight into MATLAB.

MATLAB's deep learning capabilities have been overshadowed by TensorFlow and PyTorch. I'm a MATLAB fanatic and though it pains me to say it, MATLAB has been historically too restrictive for DL practitioners looking to really innovate in the field. However, MATLAB is very good for rapid prototyping for a variety of applications (DL included; the Deep Learning tools and apps really do make it a lot easier to prototype with different experimental setups etc) but they still lag the popular frameworks in terms of SOTA implementations etc. MATLAB is also good to get you started with DL if you're a non-programmer and already know it, so it's got a lower relative barrier to entry and initial learning curve. So if you're an engineer looking to apply some deep learning models from the last few years then MATLAB can be enough (and even with some Cross-Framework interoperability in some limited cases), but if you're trying to solve fairly new problems then you might struggle.

These are my two cents having done the bulk of my DL research in MATLAB for my PhD, which I completed last year. Started off with the intention of learning Python and TensorFlow for my DL research but ultimately chose MATLAB for short-sighted convenience reasons that cost me the opportunity to learn the better tools/frameworks over the medium term.