Algorithms are important but are especially powerful in combination of knowing computer architecture and programming language intricacies. Many years ago I was asked to look at the program written in C++ that calculated Kendall-tau correlation matrix for a large amount of data. Basically Kendall Tau is a robust replacement for Pearson correlation and it had to be calculated for 0.5M^2 elements and calculation of each…
I work on a pretty heavy ML system, which a year ago took about a month to 'fully' train. Today we're down to 25 hours training time using the same hardware, based mostly on improvements to the model. The improvements are coming from a combination of signal processing tricks and tricksy model changes. Inference is similarly much faster, too...