On what level do you want to develop and understand the system? If you work directly with Theano/TensorFlow/CNTK/MXNet, you are pretty low-level. You more or le

On what level do you want to develop and understand the system? If you work directly with Theano/TensorFlow/CNTK/MXNet, you are pretty low-level. You more or less write down the formulas from papers / books, you let the framework take the gradient of some loss, and you take care of everything, like updating the parameters according to some update rule / optimization method like SGD. See some of the tutorials of those frameworks and just decide on your own what you prefer. If you want to go more high-level, use sth like Keras. You define your network structure as a series of layer types, or maybe in other ways, and it does most of the logic for you, and has already implemented most the commonly used Deep Learning techniques. So you concentrate more on the network structure, about what techniques you want to use, etc. And Keras actually supports several backends such as Theano and TensorFlow and CNTK is work-in-progress, although as a user, you won't notice so much difference, except that maybe one backend is faster than the other or does not support some specific functionality or so.