Explore >> Select a destination
|
You are here 
|
bartwronski.com | ||
| | | | |
thenumb.at
|
|
| | | | | [AI summary] The text discusses the representation of functions as vectors and their applications in various domains such as signal processing, geometry, and physics. It explains how functions can be treated as vectors in a vector space, leading to the concept of eigenfunctions and eigenvalues, which are crucial for understanding and manipulating signals and geometries. The text also covers different types of Laplacians, including the standard Laplacian, higher-dimensional Laplacians, and the Laplace-Beltrami operator, and their applications in fields like image compression, computer graphics, and quantum mechanics. The discussion includes spherical harmonics, which are used in representing functions on spheres, and their applications in game engines and glo... | |
| | | | |
www.jeremykun.com
|
|
| | | | | The singular value decomposition (SVD) of a matrix is a fundamental tool in computer science, data analysis, and statistics. It's used for all kinds of applications from regression to prediction, to finding approximate solutions to optimization problems. In this series of two posts we'll motivate, define, compute, and use the singular value decomposition to analyze some data. (Jump to the second post) I want to spend the first post entirely on motivation and background. | |
| | | | |
algassert.com
|
|
| | | | | Craig Gidney's computer science blog | |
| | | | |
atos.net
|
|
| | | GraphRAG Generative AI integrates structured knowledge graphs during retrieval, enriching knowledge and discovering relationships in data. | ||
