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TensorFlow: learning functions at scale

Published:04 September 2016Publication History
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Abstract

TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. Its computational model is based on dataflow graphs with mutable state. Graph nodes may be mapped to different machines in a cluster, and within each machine to CPUs, GPUs, and other devices. TensorFlow supports a variety of applications, but it particularly targets training and inference with deep neural networks. It serves as a platform for research and for deploying machine learning systems across many areas, such as speech recognition, computer vision, robotics, information retrieval, and natural language processing. In this talk, we describe TensorFlow and outline some of its applications. We also discuss the question of what TensorFlow and deep learning may have to do with functional programming. Although TensorFlow is not purely functional, many of its uses are concerned with optimizing functions (during training), then with applying those functions (during inference). These functions are defined as compositions of simple primitives (as is common in functional programming), with internal data representations that are learned rather than manually designed. TensorFlow is joint work with many other people in the Google Brain team and elsewhere. More information is available at tensorflow.org.

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              cover image ACM SIGPLAN Notices
              ACM SIGPLAN Notices  Volume 51, Issue 9
              ICFP '16
              September 2016
              501 pages
              ISSN:0362-1340
              EISSN:1558-1160
              DOI:10.1145/3022670
              Issue’s Table of Contents
              • cover image ACM Conferences
                ICFP 2016: Proceedings of the 21st ACM SIGPLAN International Conference on Functional Programming
                September 2016
                501 pages
                ISBN:9781450342193
                DOI:10.1145/2951913

              Copyright © 2016 Owner/Author

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              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 4 September 2016

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