16. Projection Matrices and Least Squares

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16. Projection Matrices and Least Squares

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15. Projections onto Subspaces

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This is called "A Gentle Introduction to the Hessian Matrix"

Hessians are somewhere between #linearalgebra #calculus and #rstats but still a core aspect of #datascience

All in all, building and deriving things like these are probably only useful when developing a unique solution. For the vast majority of cases, having a general understanding is enough.

... actually, I am pretty sure that there is a #python library for just such an occasion (I have never looked though so ymmv)

14. Orthogonal Vectors and Subspaces

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Okay. After that bit of hilarity yesterday, have some stuff on #linearalgebra

Not a formula sheet but still useful for developing your #datascience intuition

12. Graphs, Networks, Incidence Matrices

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11. Matrix Spaces; Rank 1; Small World Graphs

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10. The Four Fundamental Subspaces

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Here's a question: let \(M\) be a \(0\times 0\) matrix with entries in the field \(\mathbb{F}\). What is \(\det(M)\)?

#Mathematics #Determinant #LinearAlgebra #Matrix

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