Survey: Tensorflow in Machine Learning

Abstract

TensorFlow(TF) is a large-scale ML system that can be used in many different situations. Data-flow graphs are used by TF to define operations, shared state, and computations that change that state. It distributes the nodes of a Data-flow graph across multiple machines in a cluster, as well as diverse processing units within a single system, such as multicore CPUs, general-purpose GPUs and Tensor Processing Units, which are custom-designed ASICs (TPUs). Developers may use TF to try out new training techniques and optimizations. It covers a broad applications range, which focus on inference and DNN training. It is utilised in production by several Google services, and it has since been released as an open-source project and is broadly used in ML research. We review TensorFlow and place it in the context of modern deep learning techniques and technologies in this paper. We go over the fundamental computational concepts and how they work. The distributed execution concept, as well as its programming interface as accompanying visualisation toolkit. The remainder of this paper will provide a comprehensive overview of TensorFlow and place it in the context of machine learning today