In today's rapidly advancing technological era, mathematics is undoubtedly the cornerstone of intelligent systems. From the laws of physics to the core algorithms of artificial intelligence, ...
In the previous chapter, we learned various strategies to guide AI models 'down the mountain' (optimization algorithms), such as SGD and Adam. The core of these strategies relies on a key piece of ...
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: We introduce, for the first time in wireless communication networks, a quantum gradient descent (QGD) algorithm to maximize sum data rates in non-orthogonal multiple access (NOMA)-based ...
ABSTRACT: This paper proposes a unique approach to load forecasting using a fast convergent artificial neural network (ANN) and is driven by the critical need for power system planning. The Mazoon ...
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 learning ...
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 ...
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