One of the key mathematical foundations of machine learning is using gradient descent to find maxima and minima in a multi-dimensional data set. Gradient descent is good, but getting the most out of it can sometimes leave you wringing your hands or doing a lot of painful mathematical investigation and analysis. Investigations that can quickly tax even a mathematics major.
Sergui Puscas shows us a different, more intuitive way to find maxima and minima by using swarming and flocking techniques. It’s a pretty fun read.