In this talk, I will explain how to compute both Hodge numbers for complete intersection Calabi-Yau (CICY) 3- and 4-folds using machine learning. I will first make a tour of various machine learning algorithms and explain how exploratory data analysis can help in improving results. Then, I will describe a neural network inspired from the Google's Inception model which, for the 3-folds, reaches nearly perfect accuracy for h11 using much fewer data and parameters compared to other approaches. I will then describe an improved architecture to compute 3 out of 4 Hodge numbers for the 4-folds with more than 95% accuracy. I will conclude by describing how more recent techniques could improve the computations of the remaining Hodge numbers and extract analytic information from the network.
arxiv: 2007.13379, 2007.15706, 2108.02221
