Microsoft Lifts Restrictions On Deep Learning Toolkit, Moves To GitHub


Microsoft’s deep learning toolkit is now open to more developers who want to create deep learning models for speech and image recognition

Microsoft has shifted its Computational Network Toolkit (CNTK), its deep learning application builder, over to code repository GitHub.

CNTK first went open source in April 2015, but was hosted on Microsoft’s CodePlex repository, where users had to adhere to Microsoft’s academic license.


Xuedong Huang (Photography by Scott Eklund/Red Box Pictures)

But now that the open source code for CNTK is on GitHub, developers will be able to use the platform under the MIT open source license – a license that lifts many restrictions on reuse by third party developers for their own needs.

CNTK, which effectively lets developers create deep learning models for speech and image recognition, its reportedly “more efficient” than four other popular competitors.

Xuedong Huang, Microsoft’s chief speech scientist, wrote in a Microsoft blog: “The CNTK toolkit is just insanely more efficient than anything we have ever seen.”

Researchers at Microsoft claim CNTK could be useful to anyone from deep learning startups to more established companies that are processing a lot of data in real time.

“With CNTK, they can actually join us to drive artificial intelligence breakthroughs,” Huang said.

Last November, Microsoft made its machine learning toolkit openly available to the entire developer community, the firm announced.

The news follows steps made by Google to promote its machine learning platform, TensorFlow, this week by making it open source.

Microsoft’s toolkit, officially called the Microsoft Distributed Machine Learning Toolkit (DMTK), will be available on code repository database GitHub, and is designed to use multiple computers wired in parallel to solve complex computing problems.

And in December of last year, Facebook open-sourced its own AI hardware design.

“At Facebook, we’ve made great progress with off-the-shelf infrastructure components and design thus far. We’ve developed software that can read stories, answer questions about scenes, play games, and even learn unspecified tasks through observing some examples,” said Facebook.

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