tensorflow tensorflow is an open source software library for machine learning released by google in november tensorflow is based on the internal system that google uses to power its research and production systems tensorflow is quite similar to theano and can be considered as google' attempt to provide an upgrade to theano by providing easy-to-use interfaces into deep learningneural networksand machine learning with strong focus on rapid prototyping and model deployment constructs like theano it also provides constructs for symbolical mathematicswhich are then translated into computational graphs these graphs are then compiled into lower-level code and executed efficiently like theanotensorflow also supports cpus and gpus seamlessly in fact tensorflow works best on tpuknown as the tensor processing unitwhich was invented by google in addition to having python apitensorflow is also exposed by apis to ++haskelljavaand go languages one of the major differences tensorflow has as compared to theano is the support for higher-level operationswhich ease the process of machine learning and its focus on model development as well as deployment to production and model serving via multiple mechanisms (of theano is not so intuitive to usewhich is another area tensorflow aims to fillby its easy-to-understand implementations and extensive documentation the constructs provided by tensorflow are quite similar to those of theano so we will not be reiterating those you can always refer to the documentation provided for tensorflow at for more details installation tensorflow works well on linux and mac systemsbut was not directly available on windows due to internal dependencies on bazel the good news is that it was recently successfully launched for windows platforms too it requires minimum of python for its execution the library can be installed by using pip or by using conda install function note that for successful installation of tensorflowwe will also require updated dask and pandas libraries on our system conda install tensorflow once we have installed the librarywe can verify successful install by verifying it in the ipython console with the following commands in [ ]import tensorflow as tf hello tf constant('hellotensorflow!'sess tf session(print(sess run(hello) 'hellotensorflow!the message verifies our successful install of the tensorflow library you are also likely to see bunch of warning messages but you can safely ignore them the reason for those messages is the fact that the default tensorflow build is not built with support for some instruction setswhich may slow down the process of learning bit