In this video, we explore how we can factorize any rectangular matrix using the singular value decomposition and why this transformation can be useful when solving machine learning problems.
*References*
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Eigendecomposition Explained: https://youtu.be/ihUr2LbdYlE
*Related Videos*
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Multivariate Normal (Gaussian) Distribution Explained: https://youtu.be/UVvuwv-ne1I
The Bessel's Correction: https://youtu.be/E3_408q1mjo
Gradient Boosting with Regression Trees Explained: https://youtu.be/lOwsMpdjxog
P-Values Explained: https://youtu.be/IZUfbRvsZ9w
Kabsch-Umeyama Algorithm: https://youtu.be/nCs_e6fP7Jo
Eigendecomposition Explained: https://youtu.be/ihUr2LbdYlE
*Contents*
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00:00 - Intro
00:33 - SVD Ecquiation
01:33 - SVD Visualization
03:38 - SVD Application 1 - Image Compression
04:26 - SVD Application 2 - Recommandation Systems
05:28 - Outro
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#svd #singularvaluedecomposition #eigenvectors #eigenvalues #linearalgebra