TensorFlow 2 review: Easier machine learning

Now more platform than toolkit, TensorFlow has made strides in everything from ease of use to distributed training and deployment

At a Glance

The importance of machine learning and deep learning is no longer in doubt. After decades of promise, hype, and disappointment, both have led to practical applications. We haven’t gotten to the point where machine learning or deep learning applications are perfect, but many are very good indeed.

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Of all the excellent machine learning and deep learning frameworks available, TensorFlow is the most mature, has the most citations in research papers (even excluding citations from Google employees), and has the best story about use in production. It may not be the easiest framework to learn, but it’s much less intimidating than it was in 2016. TensorFlow underlies many Google services.

The TensorFlow 2.0 website describes the project as an “end-to-end open source machine learning platform.” The upshot is that TensorFlow has become a more comprehensive “ecosystem of tools, libraries, and community resources” that help researchers build and deploy AI-powered applications.

There are four major parts to TensorFlow 2.0:

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