Unlike the metaphorical mountaineer, optimization researchers can program their gradient descent algorithms to take steps of any size. Giant leaps are tempting but also risky, as they could overshoot ...

Find out why backpropagation and gradient descent are key to prediction in machine learning, then get started with training a simple neural network using gradient descent and Java code.

In this course, you’ll learn theoretical foundations of optimization methods used for training deep machine learning models. Why does gradient descent work? Specifically, what can we guarantee about ...

DIMITRIS VARTZIOTIS, BENJAMIN HIMPEL, EFFICIENT MESH OPTIMIZATION USING THE GRADIENT FLOW OF THE MEAN VOLUME, SIAM Journal on Numerical Analysis, Vol. 52, No. 2 (2014), pp. 1050-1075 ...

The Data Science Lab 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 ...

We improve a recent accelerated proximal gradient (APG) method in [Li, Q., Zhou, Y., Liang, Y. and Varshney, P. K., Convergence analysis of proximal gradient with momentum for nonconvex optimization, ...

A new technical paper titled “Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent” was published by researchers at Imperial College London. Abstract “The ...