Deep neural networks can solve the most challenging problems, but require abundant computing power and massive amounts of data.

The learning algorithm that enables the runaway success of deep neural networks doesn’t work in biological brains, but researchers are finding alternatives that could.

Neuton is a neural network framework, which Bell Integrator claims is far more effective than any other framework and non-neural algorithm available on the market.

The algorithm taps into these human capabilities via “an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality.

Artificial neural networks, the underlying structure of deep learning algorithms, roughly mimic the physical structure of the human brain.

Researchers have developed an algorithm to train an analog neural network just as accurately as a digital one, enabling the development of more efficient alternatives to power-hungry deep learning ...

Machine learning uses algorithms to turn a data set into a model that can identify patterns or make predictions from new data. Which algorithm works best depends on the problem.

If successful, DeepMind's goal to bridge deep learning and classical computer science could revolutionize AI and software as we know them.

How to ID an algorithm So is Stanford’s “algorithm” an algorithm? That depends how you define the term. While there’s no universally accepted definition, a common one comes from a 1971 ...