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Microsoft

Over the past few years, there has been increasing demand for introductory coursework in artificial intelligence and machine learning. Enrollment in Introduction to Machine Learning classes at universities in the US have grown as much as 12 times in the past decade[1]. These introductory courses play a key role in educating the future of machine learning professionals.

The rest of this post is meant as a follow-up to our press release highlighting our release of DirectML on WSL, NVIDIA CUDA support, and TensorFlow with DirectML package. Learn more

 

DirectML for Students and Beginners

The DirectML teams wants to ensure that the Windows devices students and beginners  use can fully support their coursework.  Our solution starts by providing hardware accelerated training on the breadth of Windows hardware, across DirectX 12 based GPU, via DirectML. The DirectML API enables GPU accelerated training and inferencing for machine learning models.dx12+dml.png

TensorFlow + DirectML

In June 2020, we took our first step to enable future professionals to leverage their current hardware across the breadth of the Windows ecosystem by releasing a preview package of TensorFlow with a DirectML backend.

Through this package, we are meeting students and beginners where they are by providing support for the models they want in a frameworks they use, all while making the most of their existing GPU hardware.

Students and beginners can start with the TensorFlow tutorial models or our examples to start building the foundation for their future.

 

Engage with our Community

 

Frame 1.png 

 

If you try out the TensorFlow with DirectML package and run into issues, we would love to hear your feedback on our DirectML GitHub repo.

We are looking to engage with the community of educators to better understand key scenarios for students and beginners getting into machine learning.  Share your thoughts in this survey to shape investments we make with DirectML to provide GPU hardware acceleration to additional ML tools and frameworks.

 

 

[1] https://hai.stanford.edu/sites/default/files/ai_index_2019_report.pdf