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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 ...
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 ...
IEEE

Online Learning of Noisy Functions via a Data-Regularized Gradient-Descent Approach

Abstract: Online first-order algorithms for function identification and regression with noisy data often rely on replacing actual gradients with their constructed noisy estimates. Stochastic gradient ...
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 ...
IEEE

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 ...
GitHub

Pull requests: hectormikel/Gradient-Descent-and-Cost-Function-for-Test-Scores

Pull requests help you collaborate on code with other people. As pull requests are created, they’ll appear here in a searchable and filterable list. To get started, you should create a pull request.
Scientific Research Publishing

Ruder, S. (2017) An Overview of Gradient Descent Optimization Algorithms. arXiv: 1609.04747.

ABSTRACT: As drivers age, roadway conditions may become more challenging, particularly when normal aging is coupled with cognitive decline. Driving during lower visibility conditions, such as ...