[octbash]

Minimum=/=lowest effort.

Especially with the caveat of "with a programming background", it is far easier to reason and debug through PyTorch with just Python knowledge, compared to TensorFlow/Keras, which sooner or later requires you to learn a condensed history of TensorFlow/Keras development to understand why things are the way they are.

In my opinion,

  import lib
  lib.train("imagenet", "resnet50", epochs=10)
  lib.eval()

is NOT a good example of a beginner friendly library. It's a thin wrapper facade that hides all of the actual complexity behind "Train ImageNet in 3 lines of code!"

[minimaxir]

Fair; maybe minimum isn't the right word. More like "minimum without full abstraction."

The Keras examples are a good reference (e.g. https://www.tensorflow.org/tutorials/keras/classification ); even without an AI background, you have a sense of both what's going on how to tweak the model to improve it.

[tastyminerals]

The reason why Keras became so popular is that it borrowed a lot of concepts from Lua Torch (which predates even Theano). And anyone who worked with Torch immediately sees it reading Keras code. But Torch was Lua and naturally it received less recognition than it deserved. Your will not lose anything by simply moving to PyTorch.