Why is TensorFlow considered important in Data Science?

TensorFlow can be used to develop models for various tasks, including natural language processing, image recognition, handwriting recognition, and different computational-based simulations such as partial differential equations.

 

The key benefits of TensorFlow are in its ability to execute low-level operations across many acceleration platforms, automatic computation of gradients, production-level scalability, and interoperable graph exportation. By providing Keras as a high-level API and eager execution as an alternative to the dataflow paradigm on TensorFlow, it’s always easy to write code comfortably.

 

As the original developer of TensorFlow, Google still strongly backs the library and has catalyzed the rapid pace of its development. For example Google has created an online hub for sharing the many different models created by users.