Mini Batch Gradient Descent is an algorithm that helps to speed up learning while dealing with a large dataset. Instead of ...

Understand what is Linear Regression Gradient Descent in Machine Learning and how it is used. Linear Regression Gradient ...

Abstract: The gradient descent bit-flipping with momentum (GDBF-w/M) and probabilistic GDBF-w/M (PGDBF-w/M) algorithms significantly improve the decoding performance of the bit-flipping (BF) algorithm ...

Abstract: Distributed gradient descent algorithms have come to the fore in modern machine learning, especially in parallelizing the handling of large datasets that are distributed across several ...

Cryptography secures communication in banking, messaging, and blockchain. Good algorithms (AES, RSA, ECC, SHA-2/3, ChaCha20) are secure, efficient, and widely trusted. Bad algorithms (DES, MD5, SHA-1, ...

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 ...

Stochastic gradient descent (SGD) provides a scalable way to compute parameter estimates in applications involving large-scale data or streaming data. As an alternative version, averaged implicit SGD ...

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 ...

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 ...

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 ...