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

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 ...

This includes grid search, stochastic gradient descent (SGD) algorithms, kernel ridge regression methods, and in-memory distributed parallel computations in deep neural networks (DNNs). Ridge ...

About this project This project explores linear regression using both the least squares method and gradient descent. It implements the matrix form of linear regression and applies it to a real-world ...

In the NeurIPS 2022 Outstanding Paper Gradient Descent: The Ultimate Optimizer, MIT CSAIL and Meta researchers present a novel technique that enables gradient descent optimizers such as SGD and Adam ...

Aitken gradient descent (AGD) algorithm takes some advantages over the standard gradient descent and Newton methods: 1) can achieve at least quadratic convergence in general; 2) does not require the ...

This was just a fun experiment to use Gradient Descent to perform linear regression. Linear regression is all about finding the line of best fit of all given data points.