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