What is TensorFlow’s role in machine learning?

TensorFlow is Google's popular contribution to machine learning work. It consists of an open source set of resources with data flow graphs that helps to model algorithms and build neural networks and other machine learning technologies.

In coordination with programming languages like Python and various types of libraries such as Numpy, TensorFlow helps engineers to design machine learning systems effectively. The software library has its own collection of data flow graphs that help with machine learning classification, perception, and discovery. (Read Why is TensorFlow so popular for machine learning systems?)

TensorFlow is a Python library. It can be used to create deep learning models, or other wrapper libraries can be built on top of TensorFlow , Essentially, TensorFlow offers a key piece of an ecosystem of tools, libraries and community resources for innovation. It's a user-friendly way to spin up technologies for image recognition or other modern uses.

Companies using TensorFlow include AirBnB, eBay, Intel, Uber, Snapchat, Twitter and IBM, among many others.

TensorFlow runs in a number of different environments. Looking at the utility of TensorFlow enables beginners to start to understand how libraries and resources contribute to machine learning projects. The data flow graphs are a way to visualize the mathematical computations and operations that occur in machine learning.

That utility makes TensorFlow a popular part of a developer’s toolkit.

Check out: TensorFlow: 6 Courses to Become an Open-Source ML Framework Pro