Since 2012, “deep learning” has mostly meant using back-propagation to optimize all the parameters in a deep computational graph representing some differentiable function.

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 ...

Rebooting AI: Deep learning, meet knowledge graphs Knowledge graphs, the 20-year old hype, may have something to offer there. Written by George Anadiotis, Contributor Nov. 20, 2020 at 7:49 a.m. PT ...

DeepMind is trying to combine deep learning and algorithms, creating the one algorithm to rule them all: a deep learning model that can learn how to emulate any algorithm, generating an algorithm ...

AnzoGraph has extensions to SPARQL to support graph algorithms, inferencing, window aggregates, BI functions, and named views. Support for openCypher and Bolt (the Neo4j protocol) is planned ...

The Stanford team’s deep-learning algorithm, called UrbanDenoiser, has been trained on data sets of 80,000 samples of urban seismic noise and 33,751 samples that indicate earthquake activity.

Artificial intelligence, machine learning, and deep learning have become integral for many businesses. But, the terms are often used interchangeably. Here's how to tell them apart.

Deep learning has also benefited from the company’s method of splitting computing tasks among many machines so they can be done much more quickly. That’s a technology Dean helped develop ...