I work in Data Science/Deep Learning and I studied Linear Algebra (null spaces, Least Squares, SVD, etc.) at the University, but quite some years have passed and I can't remember all details. Another example of a topic I would like to refresh: when the training set for a machine learning grows in size, not only each training iteration takes longer (this is trivial - more data points to process at each iteration) but also more iterations are needed to get to convergence. I guess this could be related to the condition number of the coefficient matrices of the linear systems we solve during training - bigger matrix size often corresponds to a larger ratio between the maximum and minimum (in absolute value) eigenvalues. I'm an engineer, so I'm looking for a clear and reasonably rigorous reference, but probably not at the level of a math grad student.
Given my goals, is this book a good reference?
http://math.mit.edu/~gs/linearalgebra/
If not, can you suggest a better reference?