Factoring Massive Numbers with Machine Learning Techniques

Here you will also learn how data science techniques are applied to big data, including visualization, to derive insights. Here, we use techniques of big data, statistics, and machine learning - in short a data science approach - to hopefully discover new efficient factoring techniques for these massive numbers. The remarkable fact here is that convergence occurs in many cases (albeit rarely), sometimes in as little as 2 or 3 iterations, sometimes for a large number of starting points (the number s in step 1) despite the fact that we are dealing with highly chaotic structures that behave almost randomly - something very different from the classic fixed-point theorem. So, pattern recognition techniques can be used here to further optimize the algorithm, and for instance, to identify the optimal threshold for the maximum number of iterations allowed (here, the number 20 is arbitrary.).