Projects with this topic
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A comprehensive repository of 100+ Jupyter Notebooks covering linear algebra topics, including matrix decompositions, Gaussian elimination, eigenvalues, SVD, and interactive Python/Julia examples for teaching and learning.
Check out our YouTube channel https://www.youtube.com/playlist?list=PLBDUlnmEqyXD_llq6wETUqRkJGRiu8wFU for tutorials and walkthroughs of these notebooks.
linear algebra Python julia-language holoviews Jupyter note... Gaussian eli... Inverse determinant Wedge Product eigenproblem Rayleigh Coe... bilinear Hil... fourier tran... basis Diagonalization QR decompostion SVD eigenvectors eigenvalues Jordan Form matrix decom... interactive ... Rayleigh Quo... generalized ... matrices elementary-m... Vector Spaces dimension of... orthogonality orthonormal ... change of basis matrix inverse decompression cholesky dec... eigenvalues ... generalized ... Moore-Penros... regularized ... Thikonov reg... matrix pencil constrained ... matrix funct... Principal Co... pca Dimensionali... data compres... image compre... graph Laplacian least squares coordinate-t... linear algeb... CCA Canonical Co... Krylov space Arnoldi algo... Lancosz algo... Ritz eigenva... Matrix deriv... Grassmannian Principal an...Updated -
This thesis addresses the challenges of modernizing Germany's power grid, quantifying risks posed by renewable energy in terms of grid stability. Stability measures are defined. Variability is quantified by fitting Gaussian Mixture Models (GMM) Principal on the Component Analysis (PCA) which is used for dimensionality reduction. Using MCMC techniques find low-probability patterns linked to blackouts and desynchronization.
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Python libraries for Principal Component Analysis-based (PCA) model order reduction, clustering and data analysis.
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Perform PCA followed by k-means and hierarchical clustering analysis on the Breast Cancer Wisconsin data set
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