Why did Google make TensorFlow open source?

TensorFlow has been developed within Google for its own uses, though it is now to shared within an Open Source community. Why Is TensorFlow Open Sourced? According to a TensorFlow website, the open source software will spur innovation and make it easier for researchers to share their ideas and code.

What does Google use TensorFlow for?

Tensorflow is used internally at Google to power all of its machine learning and AI. Google’s data centers are powered using AI and TensorFlow to help optimize the usage of these data centers to reduce bandwidth, to ensure network connections are optimized, and to reduce power consumption.

Did Google develop TensorFlow?

TensorFlow was developed by the Google Brain team for internal Google use in research and production. The initial version was released under the Apache License 2.0 in 2015.

Why is Google open sourcing TensorFlow?

By open sourcing TensorFlow, Google gave this community access to a platform it backs to power their research. This makes migrating the world’s algorithms from other deep learning tools onto TensorFlow theoretically possible. AI as a trend is clearly here to stay and Google wants a platform that leads this trend.

What is TensorFlow and why is it so popular?

TensorFlow was part of a former Google product called DistBelief. DistBelief was responsible for a program called DeepDream. The program was built for scientists and engineers to visualise how deep neural networks process images. As fate would have it, the algorithm went viral and everyone started visualising abstract and psychedelic art in it.

Where can I find TensorFlow on GitHub?

In the TensorFlow GitHub org, you can find not only TensorFlow itself, but a useful ecosystem of other repos, including models, serving, TensorBoard, Project Magenta, and many more. (A few of these are described below).

What are the supported TensorFlow algorithms in Python?

Below are the supported TensorFlow algorithms list: Currently, TensorFlow 1.10 has a built-in API for: In the first two line of code, we have imported tensorflow as tf. With Python, it is a common practice to use a short name for a library. The advantage is to avoid to type the full name of the library when we need to use it.