Generative AI and Engineering Applications Lectures

Lecture 1. Introduction to Machine Learning
Lecture 2. Back Propagation and Training Process
Lecture 3. Back Propagation and Cost Function
Lecture 4. [Project] GPT-collaborated GA-BO for Optimal PDN Design of HBM
Lecture 5. [Project] GPT-collaborated BO for EMIB Design with DNN Estimation
Lecture 6. Loss Function of Generative AI (Entropy & KL Divergence)
Lecture 7. [Project] GPT-collaborated IL for Hybrid Bonding TSV
Lecture 8. [Project] GPT-collaborated DRL Channel/EQ Design in HBM
Lecture 9. Generative AI Architecture & Entropy Loss Function
Lecture 10. Overall Perspectives of Generative AI Architecture
Lecture 11. Sequential generative AI with RNN
Lecture 12. Back Propagations in RNN
Lecture 13. Attention is all you need
Lecture 14. Multi-head attentions in transformer encoder
Lecture 15. Cross-attentions in transformer encoder-decoder
Lecture 16. Generative AI for Image, Generative Adversarial Network (GAN) Architecture & Loss Functions
Lecture 17. Variational Autoencoder (VAE) Architecture
Lecture 18. Variational Autoencoder (VAE) Loss Function and Image Generation
Lecture 19. Adaptive Gramian-Angular-Field Segmentation Integration Based Generative Adversarial Networks (AGSI-GAN) for Eye Diagram Estimation of High Bandwidth Memory (HBM) Interposer
Lecture 20. DevFormer: A Symmetric Transformer for Context-aware Decoupling Capacitor Placement
Lecture 21. Diffusion Model for Image Generation
Lecture 22. Reinforcement Learning for Fine Tuning and Human Feedback
Lecture 23. Markov Property and Bellman Equation
Lecture 24. Monte Carlo Method & Temporal Difference Method
Lecture 25. Deep Reinforcement Learning for Value Agent Network
Lecture 26. Deep Reinforcement Learning for Policy Agent Network