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We introduce a neural network inspired by Google's Inception model to computethe Hodge number $h^{1,1}$ of complete intersection Calabi-Yau (CICY) 3-folds.This architecture improves largely the accuracy of the predictions overexisting results, giving already 97% of accuracy with just 30% of the data fortraining. Moreover, accuracy climbs to 99% when using 80% of the data fortraining. This proves that neural networks are a valuable resource to studygeometric aspects in both pure mathematics and string theory.

Inception Neural Network for Complete Intersection Calabi-Yau 3-folds