Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and data preprocessing. If you''ve ever built a predictive model, worked on a ...

Abstract: Optimal inverse design, including topology optimization and evaluation of fundamental bounds on performance, which was introduced in Part 1, is applied to various antenna design problems. A ...

This framework provides a comprehensive set of tools and utilities for implementing and experimenting with Extreme Learning Machines using Python and TensorFlow. ELMs are a type of machine learning ...

Why write ten lines of code when one will do? From magic variable swaps to high-speed data counting, these Python snippets ...

A marriage of formal methods and LLMs seeks to harness the strengths of both.

A Python implementation of the Mobilise-D algorithm pipeline for gait analysis using IMU worn at the lower back (Learn more about the Mobilise-D project). This package is meant as reference ...

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions or values from labeled historical data, enabling precise signals such as ...

Abstract: Microwave Imaging is a key technique for reconstructing the electrical properties of inaccessible media, relying on algorithms to solve the associated Electromagnetic Inverse Scattering ...

From the Department of Bizarre Anomalies: Microsoft has suppressed an unexplained anomaly on its network that was routing traffic destined to example.com—a domain reserved for testing purposes—to a ...