Figuring out the ways in which algorithms and deep learning models are different is a good start if the goal is to reconcile them. Deep learning can’t generalize For starters, Blundell said ...

Deep learning opens a new level of capabilities within the artificial intelligence realm, but its use has been limited to data scientists. Nowadays, finally, it may be ripe for "democratization ...

Algorithmia today is adding 15 deep-learning algorithms to its marketplace of roughly 2,000 callable APIs of all kinds, said Diego Oppenheimer, Algorithmia’s founder and CEO.

Deep learning applications. There are many examples of problems that currently require deep learning to produce the best models. Natural language processing (NLP) is a good one.

Last week we described the next stage of deep learning hardware developments in some detail, focusing on a few specific architectures that capture what the rapidly-evolving field of machine learning ...

Deep learning is gaining traction across a broad swath of applications, providing more nuanced and complex behavior than machine learning offers today. Those attributes are particularly important for ...

Deep Learning Applications. Deep learning helps AI tools learn and perform tasks like detecting images and objects with high accuracy. As deep learning algorithms become more sophisticated, ...

Deep learning takes advantage of “algorithms that learn by using a large, many-layered collection of connected processes and exposing these processors to a vast set of examples,” Adams adds. These ...