I recently joined a Discord channel about Mechanistic Interpretability and within that joined a small reading group for the book Mathematics for Machine Learning, to brush up on my mathematical background. For our first session, we read the first fifty pages, which covers the basics of vectors and vector spaces, solving linear equations, reduced echelon form and Gaussian elimination. I was surprised by this, because I was expecting there to be more emphasis on conceptual understanding rather than on computations: knowing how to reduce a matrix to echelon form is not central to linear algebra or machine learning.
Vectors are not a list of numbers
Vectors are not a list of numbers
Vectors are not a list of numbers
I recently joined a Discord channel about Mechanistic Interpretability and within that joined a small reading group for the book Mathematics for Machine Learning, to brush up on my mathematical background. For our first session, we read the first fifty pages, which covers the basics of vectors and vector spaces, solving linear equations, reduced echelon form and Gaussian elimination. I was surprised by this, because I was expecting there to be more emphasis on conceptual understanding rather than on computations: knowing how to reduce a matrix to echelon form is not central to linear algebra or machine learning.