[stared]

Yes, for all practical problems data is the biggest challenge.

Though, debugging matters. In TF it is easy to get errors and spend a lot of time searching for them. In PyTorch it is straightforward. It matters the most when the network, or cost function, is not standard (think: YOLO architecture).

E.g. when I wanted to write some differentiable decision tree it took me way longer in TF (I already knew) than with PyTorch, having its tutorial on another pane.

[bitL]

TensorFlow needs some Deep Learning-based assistant to identify common cause of errors you might see on the AST level. Cryptic errors are its weakness and an AI trained to spot correlations between Python AST and error might be very helpful.

[lord_ring_111]

Tensorflow also does not work without needing to build urself for my 2010 or so cpu. So i ended up trying pytorch and i am glad i did. Liking it better than esoteric errors.