Mathematical Foundations of Data Science covers high-dimensional analysis and deep learning

This comprehensive text formalizes the mathematical basis of data science, covering topics from singular value decomposition and random projections to matrix concentration inequalities and deep learning theory. It connects high-dimensional geometry and optimization to core machine learning tasks.