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www.oranlooney.com | ||
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matthewmcateer.me
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| | | | | Important mathematical prerequisites for getting into Machine Learning, Deep Learning, or any of the other space | |
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peterbloem.nl
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| | | | | [AI summary] This text provides an in-depth exploration of Principal Component Analysis (PCA), focusing on its mathematical foundations, optimization objectives, and practical implementation. The key points are as follows: 1. PCA is equivalent to maximizing variance or minimizing reconstruction error, depending on the formulation. 2. The eigenvectors of the covariance matrix $\mathbf{S}$ represent the principal components, which form an orthonormal basis for the data. 3. The spectral theorem ensures the existence of such a decomposition for symmetric matrices, which is critical for PCA. 4. The optimization problem for PCA can be solved efficiently using eigendecomposition or singular value decomposition (SVD). 5. The text also touches on the implications of ... | |
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fa.bianp.net
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| | | | | The Langevin algorithm is a simple and powerful method to sample from a probability distribution. It's a key ingredient of some machine learning methods such as diffusion models and differentially private learning. In this post, I'll derive a simple convergence analysis of this method in the special case when the ... | |
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michaelscodingspot.com
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| | | Michael Shpilt's Blog on .NET software development, C#, performance, debugging, and programming productivity | ||
