As with most programming questions, the answer is a combination of yes and no, and that depends on the level of abstraction provided by the framework. I starte

As with most programming questions, the answer is a combination of yes and no, and that depends on the level of abstraction provided by the framework. I started off using Caffe/Torch and currently use mostly Keras for most of my deep learning related experiments. With a more base level framework, I actually could tinker with different moving components to understand why they are used as they are, while with a higher level abstraction, I can concentrate on the problem at hand, knowing that most basic abstractions (or building blocks) are well developed already and have more or less been battle tested by people far smarter than me. And of course, when it comes to pure speed numbers and architecture for scaling/deployment, these frameworks do vary among themselves: https://github.com/zer0n/deepframeworks/blob/master/README.md https://github.com/zer0n/deepframeworks/blob/master/README.m...