There's a big trend happening in machine learning (ML) – programmers are flocking toward a tool called TensorFlow, an open-source library product that facilitates some of the key work inherent in building and using training data sets in ML. With big names adopting TensorFlow for machine learning, the popularity is evident. The question is why TensorFlow has emerged as a winner.
On one hand, there is a case to be made that some of TensorFlow's popularity is based on its origins. Developed originally by Google Brain, TensorFlow is nominally a "Google product" and so it enjoys the prestige of the household name, despite Google's move to release the software under an open-source Apache license. There are also indicators that TensorFlow has been better marketed than some of its competitors. Another factor could be big adopters; for instance, DeepMind's choice to use TensorFlow may influence other developers with a kind of "domino effect" that often ends up pushing one certain software tool into industry dominance.
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On the other hand, there are many compelling reasons why a company might want to use TensorFlow over other machine learning tools. Some of them have to do with TensorFlow's accessible and "readable" syntax, which is a must for making these programming resources easier to use. Machine learning is already such a tough hill to climb that stakeholders don't want to be wrestling with unwieldy syntax.
Other elements of TensorFlow's popularity have to do with its build: Some experts are passionate about the functionality of TensorFlow's APIs that can link out to mobile or bring better access. There's also a vibrant community supporting TensorFlow, which is another feather in its cap. Alternately, developers can look at metrics like error reduction or code iteration and find that, in many cases, using TensorFlow can decrease errors over a codebase project or help with scaling.
In addition, there is inherent functionality of TensorFlow that can also be a draw: Items like interactive logging and data visualization models, and platform options like multi-GPU support, bring even more choice to the developer's fingertips. There's a general argument that TensorFlow helps to "erase infrastructure," to virtualize machine learning and untether it from internal server farms – which is generally a big value in twenty-first century IT.
All of this factors into the immense appeal of TensorFlow for a wide spectrum of machine learning projects; the tool is used by NASA and other government agencies, as well as an impressive roster of private sector giants. The question will be what new advances TensorFlow and other utilities make possible for the future of our digital world.