If you've been following developments over the last few years, you may have noticed that deep learning and neural networks have grown wildly. Neural network architecture is able to make predictive ...

In addition to pure deep neural networks (DNNs), sometimes people use hybrid vision models, which combine deep learning with classical machine learning algorithms that perform specific sub-tasks.

This article deals with nonconvex stochastic optimization problems in deep learning. Appropriate learning rates, based on theory, for adaptive-learning-rate optimization algorithms (e.g., Adam and ...

With all the excitement over neural networks and deep-learning techniques, it’s easy to imagine that the world of computer science consists of little else. Neural networks, after all, have begun ...

Mar. 22, 2021 — Researchers developed a deep learning neural network to aid the design of soft-bodied robots. The algorithm optimizes the arrangement of sensors on the robot, enabling it to ...

Stochastic gradient (SGD) is the most used optimization algorithm to backpropagate through neural networks because it has a smaller cost than gradient descent. However, it has a very slow convergence ...

Neural networks are now applied across the spectrum of AI applications while deep learning is reserved for more specialized or advanced AI use cases. Written by eWEEK content and product ...

Born in the 1950s, the concept of an artificial neural network has progressed considerably. Today, known as “deep learning”, its uses have expanded to many areas, including finance.