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TensorFlow

TensorFlow

TensorFlow is a machine learning software library for numerical calculation founded in 2015 by Rajat Monga.

TensorFlow is an open source machine learning software library for numerical computation. It uses nodes to represent mathematical operations and graph edges represent the multidimensional data arrays or tensors communicated between them in the data graph.

Its architecture is flexible, it allows users to extend computation to one or more CPUs or GPUs in a desktop, server or mobile device with a single API.

TensorFlow was developed by researchers and engineers of Google Brain team within Google's Machine Intelligence research organization. Its original purpose was for conducting machine learning and deep neural networks research. It was applicable in a wide variety of other domains. Google initially released it on November 9, 2015 and was solidly released on December 8, 2017.

TensorFlow is the foundation of automated software, such as DeepDream and RankBrain. RankBrain now handles a significant number of search queries for Google, replacing and supplementing traditional static algorithm-based search results.

TensorFlow is written in C++ and currently has API available in Python, C++, Java and Go. It would build an computation graph and execute symbolic differentiation.

Timeline

Patents

Further Resources

Title
Author
Link
Type
Date

Machine Learning with TensorFlow

Nishant Shukla

Web

TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlow

Danijar Hafner, James Davidson, Vincent Vanhoucke

Academic paper

TensorFlow Distributions

Joshua V. Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matt Hoffman, Rif A. Saurous

Academic paper

TensorFlow: A system for large-scale machine learning

Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, Xiaoqiang Zheng

Academic paper

TensorFlow: A System for Large-Scale Machine Learning

Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean,Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur,Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker,Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng and Google Brain

Academic paper

News

Title
Author
Date
Publisher
Description
Terena Bell, Thor Olavsrud
May 4, 2021
CIO
Natural language processing is a branch of AI that enables computers to understand, process, and generate language just as people do -- and its use in business is rapidly growing.
April 21, 2021
WebWire
O'Reilly, the premier source for insight-driven learning on technology and business, today announced the results of its annual AI Adoption in the Enterprise 2021 survey. The research explores the techniques, tools, and practices enterprise organizations are using to better understand how artificial intelligence (AI) has evolved over the past year. While this year's survey generated nearly three times as many responses as last year, indicating overall industry growth, there are still cha...
Annie Siebert
April 12, 2021
TechCrunch
Industry-specific AI models are only going to boom in popularity over the next few years and businesses from across sectors will realize their power in delivering accurate and powerful insights.
Devin Coldewey
November 18, 2020
TechCrunch
A new Mac-optimized fork of machine learning environment TensorFlow posts some major performance increases. Although a big part of that is that until now the GPU wasn't used for training tasks (!), M1-based devices see even further gains, suggesting a spate of popular workflow optimizations like this one are incoming. Announced on both TensorFlow and [...]
Thor Olavsrud
November 16, 2020
CIO
Data science is a method for transforming business data into assets that help organizations improve revenue, reduce costs, seize business opportunities, improve customer experience, and more.
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References

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