Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore the intricacies of deep learning networks and their ability to adapt to intrinsic dimensionality in this seminar by Alexander Cloninger from UC San Diego. Delve into the central question of network size requirements for function approximation and how data dimensionality impacts learning. Examine ReLU networks' approximation capabilities for functions with dimensionality-reducing feature maps, focusing on projections onto low-dimensional submanifolds and distances to low-dimensional sets. Discover how deep nets remain faithful to an intrinsic dimension governed by the function rather than domain complexity. Investigate connections to two-sample testing, manifold autoencoders, and data generation. Learn about Dr. Cloninger's research in geometric data analysis and applied harmonic analysis, exploring applications in imaging, medicine, and artificial intelligence.