DRL, which fuses the strengths of deep learning and reinforcement learning, is increasingly used to support the core ...

IBM offers a slightly more comprehensible definition: Deep learning attempts to mimic the human brain—albeit far from matching its ability—enabling systems to cluster data and make predictions ...

While the dual fit of GPUs for both training and HPC simulations is convenient, other system elements do not allow such an easy marriage. Case in point. Last week we described Gordon Bell Prize ...

Those are key features missing from current deep learning systems. Deep neural networks can ingest large amounts of data and exploit huge computing resources to solve very narrow problems ...

Bengio, Hinton, and LeCun also acknowledge that current deep learning systems are still limited in the scope of problems they can solve. They perform well on specialized tasks but “are often ...

In underwater acoustics, deep learning may improve sonar systems to help detect ships and submarines in distress or in restricted waters. However, noise interference can be a challenge.

thanks to the adoption of cloud-based technology and use of deep learning systems in big data, according to Emergen Research, which expects deep learning to become a $93 billion market by 2028.

In nutshell, deep learning sits inside of machine learning, which sits inside of artificial intelligence. Artificial Intelligence: The development of a computer system which is able to perform all ...