I’ve started doing some short webinars on core math topics in machine learning for Weights & Biases, a startup that offers a really cool experiment tracking, visualization, and sharing tool.

The first webinar, How Linear Algebra is Not Like Algebra, presents Linear Algebra from a programmer’s perspective: every vector/matrix/tensor is a function, shapes are types, and matrix multiplication is composition of functions.

The second webinar Look Mom, No Indices!, introduces an index-free style of computing gradients for functions that take vectors and matrices as inputs. It’s a teaser for this blog post series.