Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single ...

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 ...

In this paper, we propose a novel approach for diffusion-based distributed quantile regression, leveraging the primal–dual hybrid gradient method to find a saddle point of a convex–concave objective.

Struggling to understand how logistic regression works with gradient descent? This video breaks down the full mathematical derivation step-by-step, so you can truly grasp this core machine ...

Linear Regression with Gradient Descent This Jupyter Notebook demonstrates a mini project on implementing Linear Regression using Gradient Descent. The project is designed to deepen the understanding ...

The notebook covers data loading, exploration, and preprocessing before implementing linear regression models. It also compares the effectiveness of least squares and gradient descent approaches, ...

Google researchers introduce PDLP (Primal-Dual Hybrid Gradient enhanced for Linear Programming), a new solver built on the restarted PDHG algorithm. PDLP uses matrix-vector multiplication instead of ...

Given a linear input/output relationship involving unknown parameters, we propose a hybrid gradient descent algorithm to estimate the unknown parameters when the inputs and the outputs are hybrid ...

Logistic regression is a classic classification method in machine learning. Classical logistic regression uses a general gradient descent method to solve the best parameters of the loss function, but ...