Description:
This textbook fills the vacuum for a senior-level academic textbook for post-GPT AI. It equips students with the mathematical underpinnings needed to effectively use deep learning and generative learning techniques in the real world. The author provides a comprehensive view of neural networks, from data-driven ML to generative pre-trained transformers, accompanied by examples and case studies that illustrate real-world solutions. Targeted to students and researchers who will eventually become part of the AI workforce, each chapter contains problem sets that help students develop insight when applying deep learning techniques, making it of great use to students and scholars alike.