Explore >> Select a destination
|
You are here 
|
www.daniellitt.com | ||
| | | | |
francisbach.com
|
|
| | | | | [AI summary] This article explores the properties of matrix relative entropy and its convexity, linking it to machine learning and information theory. It discusses the use of positive definite matrices in various contexts, including concentration inequalities and kernel methods. The article also includes a lemma on matrix cumulant generating functions and its proof, as well as references to relevant literature. | |
| | | | |
mattbaker.blog
|
|
| | | | | Test your intuition: is the following true or false? Assertion 1: If $latex A$ is a square matrix over a commutative ring $latex R$, the rows of $latex A$ are linearly independent over $latex R$ if and only if the columns of $latex A$ are linearly independent over $latex R$. (All rings in this post... | |
| | | | |
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. | |
| | | | |
sriku.org
|
|
| | | [AI summary] The article explains how to generate random numbers that follow a specific probability distribution using a uniform random number generator, focusing on methods involving inverse transform sampling and handling both continuous and discrete cases. | ||
