That is the question I set out to answer when I built Principal Component Analysis from scratch on the Breast Cancer Wisconsin dataset — 569 patients, 30 tumor measurements, two outcomes: malignant or ...
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 ...
RALEIGH is a Python implementation of the block Jacobi-conjugated gradients algorithm for computing several eigenpairs (eigenvalues and corresponding eigenvectors) of large scale real symmetric and ...
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 ...
Mass spectrometry-based lipidomics and metabolomics generate extensive data sets that, along with metadata such as clinical parameters, require specific data exploration skills to identify and ...
Treasury yield curves are often treated as black boxes—reactive to policy changes, yet rarely unpacked with intent. But when a rate shock hits, knowing how the curve flattens, steepens, or twists ...
Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. However, inferring biological processes from an NMF result still requires additional ...
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, ...
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, ...
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