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Those changes will be contested, in math as in other academic disciplines wrestling with AI’s impact. As AI models become a ...

Artificial intelligence (AI) and machine learning (ML) systems have become central to modern data-driven decision-making. They are now widely applied in fields as diverse as healthcare, finance, ...

Abstract: This work presents a computationally efficient two- stage hybrid framework for parametric Source-Mask Optimization (SMO) in advanced lithography. Gradient descent (GD) is used for mask ...

Implements Task 1. This is the sequential, single-threaded version of batch gradient descent for linear regression. It processes the entire dataset in one thread, computes gradients serially, and ...

Understand what is Linear Regression Gradient Descent in Machine Learning and how it is used. Linear Regression Gradient Descent is an algorithm we use to minimize the cost function value, so as to ...

ABSTRACT: The surge of digital data in tourism, finance and consumer markets demands predictive models capable of handling volatility, nonlinear dynamics, and long-term dependencies, where traditional ...

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 ...

Abstract: Traditional Stochastic Gradient Descent (SGD) follows a sequential update process, which can be slow and inefficient for large-scale distributed learning tasks. Parallel computing offers a ...