  We review advancements in deep learning techniques for complete intersection
Calabi-Yau (CICY) 3- and 4-folds, with the aim of understanding better how to
handle algebraic topological data with machine learning. We first discuss
methodological aspects and data analysis, before describing neural networks
architectures. Then, we describe the state-of-the art accuracy in predicting
Hodge numbers. We include new results on extrapolating predictions from low to
high Hodge numbers, and conversely.
