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nulliq.dev | ||
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www.reedbeta.com
Pixels and polygons and shaders, oh my! |
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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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codethrasher.com
A linear mapping from a vector space to a field of scalars. In other words, a linear function which acts upon a vector resulting in a real number (scalar) \begin{equation} \alpha\,:\,\mathbf{V} \longrightarrow \mathbb{R} \end{equation} Simplistically, covectors can be thought of as "row vectors", or: \begin{equation} \begin{bmatrix} 1 & 2 \end{bmatrix} \end{equation} This might look like a standard vector, which would be true in an orthonormal basis, but it is not true generally. |
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liorsinai.github.io
[AI summary] This blog post explains the different methods for representing and performing rotations in 2D space, including polar coordinates, Cartesian matrices, and complex numbers, while also introducing the concept of quaternions for future 3D rotations. |
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