When you buy through affiliate links in our content, we may earn a commission at no extra cost to you. Learn 
As one of the top tools for machine learning (ML), Google's TensorFlow open-source resource pack for neural networks and machine learning projects has a lot of practical applications in this sector.
Using this tool set with programming languages such as Python, engineers can build classification systems, fine-tune convolutional networks and work on the advancement of computer learning, including image processing and the evaluation of weighted inputs for machine learning outcomes. Offering data visualization solutions, TensorFlow can also be used with Numpy and other libraries. Unlike some types of vendor-licensed tools, the open source nature of TensorFlow is part of what has led to its modern contributions across the ML landscape.
Companies that have used TensorFlow to innovate include stakeholders in various industries, such as AirBnB, eBay, Intel, Uber, Snapchat, Twitter and IBM. IBM specifically has used TensorFlow as an element of some of its widely renowned AI models.
One of the biggest uses of TensorFlow is for deep learning, where many innovation models utilize TensorFlow as part of a tooling ecosystem.
Specifically, new advances with TensorFlow in transportation include the evolution of TensorFlow 3D, a deep learning system with GPU acceleration that is powering 3D perception for autonomous vehicles.
The use of pooling with convolutional networks is an example of TensorFlow 3D offering new package designs for cutting-edge innovation in machine learning in today's research and development world.
Another specific contribution is a package called TensorFlow Lite that is being used for on-device inference, for example with XNNPACK for sparse inference models in networks.
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. As mentioned, 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.
