I am just finishing up my MS in Statistics, and at my university they don't have any course(yet) that goes into deep learning. The only time the course briefly talk about neural net is in the context of Logit regression. And I have always wonder if the time spent proving Gauss Markov theorem is better spent somewhere else.
Do you know any source(book or otherwise) that continues to build on top of topics taught in a Regression and Linear algebra course(such as Multiple Linear, Weighted Least square, Logistic, generalized least squares, principle component regression, singular value decomposition) and slowly move towards the current state deep learning?
Do you know any source(book or otherwise) that continues to build on top of topics taught in a Regression and Linear algebra course(such as Multiple Linear, Weighted Least square, Logistic, generalized least squares, principle component regression, singular value decomposition) and slowly move towards the current state deep learning?