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    Neha Chaudhari

    TensorFlow is Google Brain’s second generation machine learning system, with a reference implementation released as open source software on November 9, 2015. While the reference implementation runs on single devices, TensorFlow can run on multiple CPUs and GPUs (with optional CUDA extensions for general-purpose computing on graphics processing units). It runs on 64-bit Linux or Mac OS X desktop or server systems, as well as on mobile computing platforms, including Android and Apple’s iOS. TensorFlow computations are expressed as stateful dataflow graphs. Many teams at Google have migrated from DistBelief to TensorFlow for research and production uses.[4]:p.2 This library of algorithms originated from Google’s need to instruct computer systems, known as neural networks, to learn and reason similarly to how humans do, so that new applications can be derived which are able to assume roles and functions previously reserved only for capable humans; the name TensorFlow itself derives from the operations which such neural networks perform on multidimensional data arrays. These multidimensional arrays are referred to as “tensors” but this concept is not identical to the mathematical concept of tensors.The purpose is to train neural networks to detect and decipher patterns and correlations.

    In June 2016, Google’s Jeff Dean said there were 1500 repositories on GitHub which mentioned TensorFlow, of which only 5 were from Google.[11]




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