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 ...

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 ...

Unlike the metaphorical mountaineer, optimization researchers can program their gradient descent algorithms to take steps of any size. Giant leaps are tempting but also risky, as they could overshoot ...

Find out why backpropagation and gradient descent are key to prediction in machine learning, then get started with training a simple neural network using gradient descent and Java code.

This paper presents an optimisation-based method using Particle Swarm Optimisation (PSO) for automatically tuning the free parameters of the Adaptive Gradient Descent-based MCA (AGDA) while accounting ...

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 ...