The K-shaped economy has returned, with gaps widening between high and low-income Americans. Higher earners see steadier opportunities, while lower-income households face cooler prospects. Some ...

Abstract: For radar signal sorting based on pulse descriptors, the inherent limitations of the traditional K-means algorithm include the requirement of a predefined number of clusters, the sensitivity ...

Rocky high steep slopes are among the most dangerous disaster-causing geological bodies in large-scale engineering projects, like water conservancy and hydropower projects, railway tunnels, and metal ...

Learn the Adagrad optimization algorithm, how it works, and how to implement it from scratch in Python for machine learning models. #Adagrad #Optimization #Python Here Are the States That Won't Tax ...

A collection of core Machine Learning algorithms implemented from scratch using only NumPy. This project focuses on understanding the inner workings of ML models without relying on libraries like ...

Accurately identifying fracture zones and their types in strata is of great significance for enhancing oil and gas recovery efficiency. Due to its complicated geological structure and long-term ...

Implement the K-Means Clustering algorithm from scratch using NumPy and visualize the results with Matplotlib. Why it's a good addition: It's a foundational unsupervised learning algorithm that fits ...

Learn how the Adadelta optimization algorithm really works by coding it from the ground up in Python. Perfect for ML enthusiasts who want to go beyond the black box! Florida State Bracing for Hefty ...

ABSTRACT: Clustering is an unsupervised machine learning technique used to organize unlabeled data into groups based on similarity. This paper applies the K-means and Fuzzy C-means clustering ...

Abstract: The k-means method is widely utilized for clustering. Its simplicity, efficacy, and swiftness make it a favored choice among clustering algorithms. It faces the challenge of sensitivity to ...