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.
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AI-Generated Summary
- Companies across various industries are using GPU-powered deep learning to extract insights from big data, with applications in areas such as self-driving cars and disease detection.
- The use of GPUs for deep learning was pioneered by computer scientist Geoff Hinton, who used them to train his neural networks, and now researchers are applying this technology to real-world problems.
- NVIDIA is developing software to help data scientists build deep learning systems powered by GPUs, including the release of cuDNN library version 3 and DIGITS 2, which was scheduled to be shipped.
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