Velocity gradients prove key to explaining large-scale magnetic field structure

All celestial bodies—planets, suns, even entire galaxies—produce magnetic fields, affecting such cosmic processes as the ...

Physics of foam strangely resembles AI training

Foams are everywhere: soap suds, shaving cream, whipped toppings and food emulsions like mayonnaise. For decades, scientists ...
Scientific Research Publishing

Boosting Algorithm: An Ensemble Learning Tool for Land Use Land Cover Classification Using Google Alpha Earth Foundations Satellite Embeddings ()

An Ensemble Learning Tool for Land Use Land Cover Classification Using Google Alpha Earth Foundations Satellite Embeddings ...
Learn With Jay on MSN

Mini-batch gradient descent in deep learning explained

Mini Batch Gradient Descent is an algorithm that helps to speed up learning while dealing with a large dataset. Instead of updating the weight parameters after assessing the entire dataset, Mini Batch ...
Learn With Jay on MSN

Linear regression using gradient descent explained simply

Understand what is Linear Regression Gradient Descent in Machine Learning and how it is used. Linear Regression Gradient Descent is an algorithm we use to minimize the cost function value, so as to ...
Scientific Research Publishing

Ruder, S. (2016) An Overview of Gradient Descent Optimization Algorithms. arXiv Preprint.

ABSTRACT: Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is ...
The New York Times

I’m Building an Algorithm That Doesn’t Rot Your Brain

Mr. Conte is the co-founder and chief executive of Patreon. The World Wide Web was introduced some three decades ago. Describing in 2019 the hopes that had been projected onto the internet at its ...
IEEE

Designing Quantum Gradient Descent Algorithm for MIMO NOMA Rate Maximization With STAR-RIS

Abstract: We introduce, for the first time in wireless communication networks, a quantum gradient descent (QGD) algorithm to maximize sum data rates in non-orthogonal multiple access (NOMA)-based ...
Visual Studio Magazine

Kernel Ridge Regression with Stochastic Gradient Descent Training Using C#

The goal of a machine learning regression problem is to predict a single numeric value. For example, you might want to predict a person's bank savings account balance based on their age, years of ...