Deep Learning for Symbolic Mathematics

This course teaches learners how to apply deep learning techniques to symbolic mathematics, with a focus on neural networks and symbolic reasoning. The course covers topics such as generating expressions randomly, counting the number of expressions, formulating formulae for counting trees and expressions, generating datasets for integration, working with ordinary differential equations, dataset cleaning and statistics, sequence decoding, evaluation, comparison with mathematical frameworks, generalization across generators, and generalization beyond the generator. The course aims to equip learners with the skills to apply deep learning in the field of symbolic mathematics using a hands-on teaching method. The intended audience for this course includes individuals interested in deep learning, symbolic mathematics, and their intersection.