NDLI: CNTK: Microsoft's Open-Source Deep-Learning Toolkit

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Big Data Needs Big Dreamers: Lessons from Successful Big Data Investors

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CNTK: Microsoft's Open-Source Deep-Learning Toolkit

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CNTK: Microsoft's Open-Source Deep-Learning Toolkit

Content Provider	ACM Digital Library
Author	Agarwal, Amit Seide, Frank
Abstract	This tutorial will introduce the Computational Network Toolkit, or CNTK, Microsoft's cutting-edge open-source deep-learning toolkit for Windows and Linux. CNTK is a powerful computation-graph based deep-learning toolkit for training and evaluating deep neural networks. Microsoft product groups use CNTK, for example to create the Cortana speech models and web ranking. CNTK supports feed-forward, convolutional, and recurrent networks for speech, image, and text workloads, also in combination. Popular network types are supported either natively (convolution) or can be described as a CNTK configuration (LSTM, sequence-to-sequence). CNTK scales to multiple GPU servers and is designed around efficiency. The tutorial will give an overview of CNTK's general architecture and describe the specific methods and algorithms used for automatic differentiation, recurrent-loop inference and execution, memory sharing, on-the-fly randomization of large corpora, and multi-server parallelization. We will then show how typical uses looks like for relevant tasks like image recognition, sequence-to-sequence modeling, and speech recognition.
Starting Page	2135
Ending Page	2135
Page Count	1
File Format	PDF MP4
ISBN	9781450342322
DOI	10.1145/2939672.2945397
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-08-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Computational networks Deep learning Cntk Neural network learning toolkit Neural networks
Content Type	Audio Text
Resource Type	Article

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