  Google's Machine Learning framework TensorFlow was open-sourced in November
2015 [1] and has since built a growing community around it. TensorFlow is
supposed to be flexible for research purposes while also allowing its models to
be deployed productively. This work is aimed towards people with experience in
Machine Learning considering whether they should use TensorFlow in their
environment. Several aspects of the framework important for such a decision are
examined, such as the heterogenity, extensibility and its computation graph. A
pure Python implementation of linear classification is compared with an
implementation utilizing TensorFlow. I also contrast TensorFlow to other
popular frameworks with respect to modeling capability, deployment and
performance and give a brief description of the current adaption of the
framework.
