herewe defined symbolical operation (denoted by the symbol zand then bound the input and the operations in function this was achieved by using the function construct provided by theano contrast it with the normal programming paradigm and we would need to define the whole function by ourselves this is one of the most powerful aspects of using symbolical mathematical package like theano using construct similar to thesewe can define complex set of operations graph structuretheano represents symbolical mathematical operations as graphs so when we define an operation like zas depicted in the earlier exampleno calculation happens instead what we get is graph representation of the expression these graphs are made up of applyopand variable nodes the apply node represents application of some op on some set of variable nodes so if we wanted to visualize the operation we defined in the preceding step as graphit would look like the depiction in figure - (sourcefigure - graph structure of theano operation theano has various low-level tensor apis for building neural network architectures using tensor arithmetic and ops this is available in the theano tensor nnet module and you can check out relevant functions at include conv for convolutional neural networks and nnet for regular neural network operations this concludes our basic introduction to theano we kept it simple because we will rarely be using theano directly and instead rely on high-level libraries like keras to build powerful deep neural networks with minimal code and focus more on solving problems efficiently and effectively