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stephenmalina.com | ||
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francisbach.com
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| | | | | [AI summary] This technical blog post explores the mathematical properties of symmetric positive definite matrices, specifically focusing on the Löwner order, matrix monotonicity, and matrix convexity in the context of machine learning and optimization. | |
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www.greaterwrong.com
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| | | | | I'm reviewing the books in the MIRI course list. It's been a while since I did a book review. The last book I reviewed was Computation and Logic, which I read in November. After that, I spent a few weeks brushing up on specific topics in preparation for my first MIRI math workshop. I read about half of The Logic of Provability and studied a little topology. I also worked my way through some relevant papers. After the workshop, I took some time off around the holidays and wrote a bit about my experience. I'm finally back into Study Mode. This week I finished Linear Algebra Done Right, by Sheldon Axler. | |
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hadrienj.github.io
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| | | | | In this post, we will see special kinds of matrix and vectors the diagonal and symmetric matrices, the unit vector and the concept of orthogonality. | |
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nhigham.com
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| | | In linear algebra terms, a correlation matrix is a symmetric positive semidefinite matrix with unit diagonal. In other words, it is a symmetric matrix with ones on the diagonal whose eigenvalues are all nonnegative. The term comes from statistics. If $latex x_1, x_2, \dots, x_n$ are column vectors with $latex m$ elements, each vector containing... | ||
