Mathematical Foundations of Deep Learning
It addresses the need for foundational mathematical understanding in deep learning for researchers and practitioners, but it is incremental as it compiles existing knowledge into a book format.
The draft book provides a comprehensive and rigorous treatment of the mathematical principles underlying modern deep learning, covering topics such as approximation capabilities, optimal control, reinforcement learning, and generative models.
This draft book offers a comprehensive and rigorous treatment of the mathematical principles underlying modern deep learning. The book spans core theoretical topics, from the approximation capabilities of deep neural networks, the theory and algorithms of optimal control and reinforcement learning integrated with deep learning techniques, to contemporary generative models that drive today's advances in artificial intelligence.