This is great news! One of the most intimidating things about getting started with deep learning if you want to understand and extend cutting-edge work is the Tower of Babel situation: aside from mastering some quite difficult and opaque concepts, you need to learn multiple frameworks in multiple languages, some of which are quite uncommon. (Want to use Torch? You have to learn Lua. Want to use Theano or Caffe? Need to learn Python too. Need to implement optimizations? Hope you&#x27;ve mastered C++.)<p>And DeepMind&#x27;s research output was a major reason to need to use Torch, and hence have to learn Lua.<p>But by switching over to TensorFlow, this means you now have one language to learn which is supported well by all the major frameworks - Python - and you can benefit from several frameworks (Theano, Keras, TensorFlow). So the language barrier is reduced and you can focus on the framework and actual NN stuff. Further, this will also drive consolidation onto TensorFlow, reducing the framework mental overhead. As long as TF is up to the job, and it reportedly is, this will benefit the deep learning community considerably.<p>I&#x27;d been wondering myself what language and framework I should focus on when I start studying NNs, and this settles it for me: Python and TensorFlow.