Nature

Convergence analysis and application for high-order neural networks based on gradient descent learning algorithm via smooth regularization

Higher order networks (HONs) extend the traditional pairwise interactions between nodes to higher order interactions involving three or more nodes. This allows for the modeling of more complex ...
Nature

Gradient descent in materia through homodyne gradient extraction

Deep learning, a multilayered neural-network approach inspired by the brain, has revolutionized machine learning. Its success relies on backpropagation, which computes gradients of a loss function for ...
InfoWorld

Styles of machine learning: Intro to neural networks

Modeled on the human brain, neural networks are one of the most common styles of machine learning. Get started with the basic design and concepts of artificial neural networks. Artificial intelligence ...
Visual Studio Magazine

Kernel Ridge Regression with Stochastic Gradient Descent Training Using JavaScript

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...
Visual Studio Magazine

Kernel Ridge Regression with Stochastic Gradient Descent Training Using C#

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...