note

Mathematical Understanding and Implementation of Principal Component Analysis (PCA) via Covariance Matrices [Python]

In data analysis and machine learning practice, "dimensionality reduction" is an essential technique for visualizing high-dimensional data and as a preprocessing step for clustering. Representative ...
GitHub

BEER: Batch EffEct Remover for single-cell data

Mar. 2021 ( V0.1.9 ): First version for Seurat 4.0.0 Feb. 2021 ( V0.1.8 ): Last version for Seurat 3.0.0 Nov. 2019 ( v0.1.7 ): In ".simple_combine(D1, D2, FILL=TRUE)", "FILL" can help users to keep ...
IEEE

In-Memory Principal Component Analysis by Crosspoint Array of Resistive Switching Memory: A new hardware approach for energy-efficient data analysis in edge computing

Abstract: In-Memory Computing (IMC) is one of the most promising candidates for data-intensive computing accelerators of machine learning (ML). A key ML algorithm for dimensionality reduction and ...
InfoWorld

The engines of AI: Machine learning algorithms explained

Machine learning and deep learning have been widely embraced, and even more widely misunderstood. In this article, I’ll step back and explain both machine learning and deep learning in basic terms, ...
InfoWorld

14 popular AI algorithms and their uses

Large language models have captured the news cycle, but there are many other kinds of machine learning and deep learning with many different use cases. Amid all the hype and hysteria about ChatGPT, ...