Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression with pseudo-inverse training implemented using JavaScript. Compared to other training techniques, such as ...
The YOLOv8 and Swin Transformer dual-module system significantly improves structural crack detection, offering a faster and ...
Learn how to implement SGD with momentum from scratch in Python—boost your optimization skills for deep learning. Mike Johnson gives update on Jan. 6 plaque Alaska received 7 feet of snow, sinking ...
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 first chapter of Neural Networks, Tricks of the Trade strongly advocates the stochastic back-propagation method to train neural networks. This is in fact an instance of a more general technique ...
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 ...
Stochastic Gradient Descent for Constrained Optimization Based on Adaptive Relaxed Barrier Functions
Abstract: This letter presents a novel stochastic gradient descent algorithm for constrained optimization. The proposed algorithm randomly samples constraints and components of the finite sum ...
Abstract: Stochastic gradient descent (SGD) and exponentiated gradient (EG) update methods are widely used in signal processing and machine learning. This study introduces a novel family of ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results