Is there a way to load a neural network from the Wolfram repository, in a platform such as TensorFlow that handles deep data more quickly than Mathematica?
Yes, the Wolfram library can export MXNet architecture and weight files:
There are open source libraries that can convert MXNet models to tensorflow models.
Also, there is newer support for ONNX model exports, but I have never tried this myself. ONNX has importers from all the major neural network libraries (tensorflow, pytorch, etc.)
Lastly, if you want to process big datasets with Mathematica, I have found this is possible if you ditch the GUI and stick to writing scripts (.m/.wl files) and running them with the wolframscript -f command line utility - like you do in python. The Wolfram/Mathematica Desktop GUI is definitely too slow - and crashy- for a lot of 'real' problems with large datasets right now :(
Thank you! Where can I find more information about the Mathematica command line?
wolframscript is documented here. You might also want to take a look at the Wolfram Engine.
I just made a brief video about using the wolframscript command line, as I find the existing educational resources somewhat lacking. First half of the video covers installation and second half covers usage:
Also note, you do not need to install wolfram engine if you already installed wolfram desktop. Wolfram engine comes with wolfram desktop. If you already have wolfram desktop installed, you can just download and install wolframscript to get access to the command line interface.