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www.johnmyleswhite.com | ||
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www.paepper.com
As a data scientist, you are dealing a lot with linear algebra and in particular the multiplication of matrices. Important properties of a matrix are its eigenvalues and corresponding eigenvectors. So let's explore those a bit to get a better intuition of what they tell you about the transformation. We will just need numpy and a plotting library and create a set of points that make up a rectangle (5 points, so they are visually connected in the plot): |
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nhigham.com
The pseudoinverse is an extension of the concept of the inverse of a nonsingular square matrix to singular matrices and rectangular matrices. It is one of many generalized inverses, but the one most useful in practice as it has a number of special properties. The pseudoinverse of a matrix $latex A\in\mathbb{C}^{m\times n}$ is an $latex... |
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aperiodical.com
If you see me doing a maths thing, I'm probably wearing one of my maths t-shirts. I've got quite a few, but the one that reliably produces the much-sought-after look of total indifferen... |
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www.reedbeta.com
When you read BRDF theory papers, you'll often see mention of slope space. Sometimes, components of the BRDF such as NDFs or masking-shadowing functions are defined in slope space, or operations are done in slope space before being converted back to ordinary vectors or polar coordinates. However, the meaning and intuition of slope space is rarely explained. Since it may not be obvious exactly what slope space is, why it is useful, or how to transform things to and from it, I thought I would write down a ... |
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