On a past personal project to learn about ML, I'd been struggling with implementing multiple variable gradient descent, and decided to write up a conceptual explanation of the method to make sure I had my head wrapped around it well enough. I referenced several sources for information about gradient descent, but most heavily influetial was Andrew Ng's Supervised Machine Learning course on Coursera.