Companies across nearly all industries are exploring how to use GPU-powered deep learning to extract insights from big data. From self-driving cars to disease-detecting mirrors, the use cases for deep learning is expanding by the day. Since computer scientist Geoff Hinton started using GPUs to train his neural networks, researchers are applying the technology to tough modeling problems in the real world.
Alex Woodie of Datanami recently interviewed Will Ramey, Accelerated Computing senior product manager at NVIDIA, to get an insight on how NVIDIA is unlocking the potential of GPU-powered deep learning applications.
The article also mentions the new software by NVIDIA aimed at helping data scientists build deep learning systems powered by GPUs will be shipped this month, which includes version 3 of the cuDNN library, and DIGITS 2.
Read more on Datanami >>
AI-Generated Summary
- GPU-powered deep learning is expanding across industries from self-driving cars to disease-detecting mirrors.
- Researchers are applying the technology to real-world modeling problems since computer scientist Geoff Hinton began using GPUs to train neural networks.
- NVIDIA is shipping new software for data scientists this month, including cuDNN version 3 and DIGITS 2.
Next Steps
- Read the Datanami article for the full interview with Will Ramey.
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