Hi folks ðŸ‘‹, I’m Arnol and this is my ML journey notebook. I try to blog about anything AI/ML-related, from theory to the latest published SOTA methods in the field.

## Singular Value Decomposition (SVD) Part 1: Math Foundations^{ [draft]}

The most common way to represent data is through matrices. As a result, this makes the methods and algorithms designed to operate on matrices very convenient for data science. We will dive into the mysteries of one of them. This method has been proven successful in matrix manipulations such as low-rank approximation, but also in more practical applications like recommender systems. This method is known as Single Value Decomposition (SVD). This blog post is the first part of a series around SVD....

## Singular Value Decomposition (SVD) Part 2: Math Foundations^{ [draft]}

write the introduction Singular Value Decomposition Singular Value Decomposition (SVD for short) is method used to factorize any rectangular matrix \(A \in \mathbb{R}^{m \times n}\) Conclusion Comming soonâ€¦ This shows as Mathjax \(a \ne b\) Likewise, this shows as Mathjax \[a \ne b\]