However, training deep learning models requires a great deal of computing power. Another drawback to deep learning is the difficulty of interpreting deep learning models.

We have written much about large-scale deep learning implementations over the last couple of years, but one question that is being posed with increasing frequency is how these workloads (training in ...

El Niño-Southern Oscillation (ENSO) is the strongest interannual variability signal in Earth's climate system. The shifts ...

SLIDE doesn’t need GPUs because it takes a fundamentally different approach to deep learning. The standard “back-propagation” training technique for deep neural networks requires matrix multiplication ...

Deep learning's availability of large data and compute power makes it far better than any of the classical machine learning algorithms.

Curious about deep learning AI? Here’s your guide on deep learning, how it works and how it is deeply associated with the artificial intelligence world.