Abstract
Deep learning is a part of artificial intelligence utilizing deep neural network architectures that have essentially progressed to the cutting edge in PC vision, speech recognition, characteristic language preparation, and different spaces. In November 2015, Google delivered TensorFlow, an open-source deep-learning programming library for characterizing, preparing, and conveying machine-learning models. In this paper, we audit Tensor Flow and put it in current deep learning ideas and programming. We discuss its essential computational standards and appropriated execution model, its programming interface, and visualization toolbox. We, at that point, contrast Tensor Flow with elective libraries, for example, Theano, Torch, or Caffe on a subjective just as a quantitative premise. Lastly, remark on watched use-instances of TensorFlow in the scholarly community and industry
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