Deep Learning
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Deep learning requires large amounts of labeled data. For example,
driverless car development requires millions of images and thousands
of hours of video.
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Deep learning requires substantial computing power. High-performance
GPUs have a parallel architecture that is efficient for deep learning.
When combined with clusters or cloud computing, this enables
development teams to reduce training time for a deep learning network
from weeks to hours or less.