Sure, I see the confusion. So basically, you bring the data, pick the neural network architecture you want to use, and set its parameters. The model trains on

Sure, I see the confusion. So basically, you bring the data, pick the neural network architecture you want to use, and set its parameters. The model trains on the data you've given it using a GPU cluster (which still takes a while) 'spanish_speech_recognizer' is the name of the model you just trained, where 'MRNN' is the actual architecture (a multiplicative recurrent neural network as described in http://www.cs.toronto.edu/~ilya/pubs/2011/LANG-RNN.pdf http://www.cs.toronto.edu/~ilya/pubs/2011/LANG-RNN.pdf) used in the example. So the models themselves aren't pre-defined, but the architectures you can use are. You can play with a lot of different parameters (if you want), but you don't have to worry about optimizing the code for GPU or making sure your implementation of the algo itself is correct. At least that's the idea.