A Tour of TensorFlow

TL;DR

This paper reviews TensorFlow, an open source deep learning library, discussing its architecture, features, and how it compares to other frameworks, highlighting its impact in research and industry.

Contribution

It provides a comprehensive overview of TensorFlow's design, capabilities, and its position among deep learning tools, including comparative analysis and practical use cases.

Findings

TensorFlow supports distributed execution and flexible programming models.

It outperforms some alternatives in certain benchmarks.

Widely adopted in academia and industry for deep learning applications.

Abstract

Deep learning is a branch of artificial intelligence employing deep neural network architectures that has significantly advanced the state-of-the-art in computer vision, speech recognition, natural language processing and other domains. In November 2015, Google released $\textit{TensorFlow}$, an open source deep learning software library for defining, training and deploying machine learning models. In this paper, we review TensorFlow and put it in context of modern deep learning concepts and software. We discuss its basic computational paradigms and distributed execution model, its programming interface as well as accompanying visualization toolkits. We then compare TensorFlow to alternative libraries such as Theano, Torch or Caffe on a qualitative as well as quantitative basis and finally comment on observed use-cases of TensorFlow in academia and industry.