All Courses
AI Career Planner
1:1 Coaching/Mentoring
Job Board
SQL
HTML/CSS/JS
Coding
Settings
LogoutThe single biggest benefit TensorFlow provides for machine learning development is abstraction. Instead of dealing with the nitty-gritty details of implementing algorithms, or figuring out proper ways to hitch the output of one function to the input of another, the developer can focus on the overall logic of the application. TensorFlow takes care of the details behind the scenes.
TensorFlow offers additional conveniences for developers who need to debug and gain introspection into TensorFlow apps. The eager execution mode lets you evaluate and modify each graph operation separately and transparently, instead of constructing the entire graph as a single opaque object and evaluating it all at once. The TensorBoard visualization suite lets you inspect and profile the way graphs run by way of an interactive, web-based dashboard.
TensorFlow also gains many advantages from the backing of an A-list commercial outfit in Google. Google has not only fueled the rapid pace of development behind the project, but created many significant offerings around TensorFlow that make it easier to deploy and easier to use: the above-mentioned TPU silicon for accelerated performance in Google’s cloud; an online hub for sharing models created with the framework; in-browser and mobile-friendly incarnations of the framework; and much more.
One caveat: Some details of TensorFlow’s implementation make it hard to obtain totally deterministic model-training results for some training jobs. Sometimes a model trained on one system will vary slightly from a model trained on another, even when they are fed the exact same data. The reasons for this are slippery—e.g., how random numbers are seeded and where, or certain non-deterministic behaviors when using GPUs). That said, it is possible to work around those issues, and TensorFlow’s team is considering more controls to affect determinism in a workflow.