Singular Value Decomposition (SVD) Explained #machinelearning #datascience #linearalgebra #stats
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YouTubeDataMListic
Singular Value Decomposition (SVD) Explained #machinelearning #datascience #linearalgebra #stats
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. *Follow Me* 🐦 Twitter: @datamlistic https://twitter.com/datamlistic 📸 Instagram: @datamlistic https://www.instagram.com/datamlistic 📱 TikTok ...
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Singular Value Decomposition (SVD) is one of the most powerful and versatile tools in linear algebra. It breaks down any matrix—even a non-square one—into three simpler matrices that reveal its fundamental structure: one that captures its directions (U), one that scales them (Σ), and one that rotates or reflects them (Vᵀ). At its core, SVD helps us understand how a matrix transforms space by identifying the principal components of that transformation. SVD is everywhere—from data science and mach
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