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Google claimed that TensorFlow's distributed architecture gives it a high level of flexibility in how coders define models that train the software. "To make TensorFlow easier to use, we have included Python libraries that make it easy to write a model that runs on a single process and scales to use multiple replicas for training".Distributed computing allows neural networks to learn much faster than the network running on one computer. Engineering leader of TensorFlow Rajat Monga said the reason why TensorFlow's multi-server version was delayed for release because they found it hard to adapt the open-source software to be usable outside of the highly customized data centers of Google. But for many researchers, its expense might as well place it in outer space.TensorFlow comes in a branch of artificial intelligence called deep learning, it works the same way human brain cells interact together.Equally, having access to the combined power of even a small cluster of computers, rather than relying on one machine, means that the overall data throughput of machine learning models and the speed at which they deliver accurate results can be accelerated.Regardless of the advanced feature, TensorFlow has already gained popularity for its software.The Verge has a report covering some of the more compelling projects that developers have created using TensorFlow.