Simplify complex datasets using Principal Component Analysis (PCA) in Python. Great for dimensionality reduction and visualization. Gingrich: Time for 'national conversation' about immigrants living ...

Abstract: In everyday life, it is crucial to protect our data and communication. The necessity for secure message communication is not a novel concept. It has existed for a long time. Data security ...

In a lawsuit, Reddit pulled back the curtain on an ecosystem of start-ups that scrape Google’s search results and resell the information to data-hungry A.I. companies. By Mike Isaac Reporting from San ...

In today’s data-rich environment, business are always looking for a way to capitalize on available data for new insights and increased efficiencies. Given the escalating volumes of data and the ...

Abstract: Kernel Principal Component Analysis (KPCA) is a nonlinear feature extraction approach, which generally needs to eigen-decompose the kernel matrix. But the size of kernel matrix scales with ...

A new multilingual tool aims to make it easier to evaluate AI models for bias in multiple languages. AI models are riddled with culturally specific biases. A new data set, called SHADES, is designed ...

What if the tools you already use could do more than you ever imagined? Picture this: you’re working on a massive dataset in Excel, trying to make sense of endless rows and columns. It’s slow, ...

This article is adapted from an edition of our Off the Charts newsletter originally published in October 2021. Off the Charts is a weekly, subscriber-only guide to The Economist’s award-winning data ...

My first encounter with a computer at the age of 10 was a serendipitous moment I could never have foreseen would define my career path. At a time when 99% of the Chinese population was unfamiliar with ...

Discover how nvmath-python leverages NVIDIA CUDA-X math libraries for high-performance matrix operations, optimizing deep learning tasks with epilog fusion, as detailed by Szymon Karpiński.