Generative AI Models and GPU Systems

This course explores the foundations and evolution of modern generative deep learning systems, taking you from latent representation learning to advanced diffusion architectures and scalable GPU deployment strategies. Combining strong conceptual depth with practical demonstrations, this course provides a structured journey through generative modeling paradigms, architectural innovations, and production-ready optimization techniques. You will begin by understanding Autoencoders and Variational Autoencoders (VAEs), examining how neural networks learn compressed latent representations and structured probabilistic spaces. From there, you will transition into Generative Adversarial Networks (GANs), analyzing adversarial training dynamics, instability challenges, and architectural improvements such as DCGAN and CycleGAN. As the course progresses, you will build a deep understanding of diffusion models — including DDPM, U-Net-based denoising systems, latent diffusion, and conditional generation techniques that power modern text-to-image systems. The course then expands into GPU systems and scalable deep learning. You will explore object detection and segmentation workloads, mixed precision training, distributed data parallel strategies, model parallelism, and production-ready GPU deployment. Through demonstrations and benchmarking exercises, you will see how modern generative systems scale efficiently while balancing memory, compute, and latency constraints. By the end of this course, you will be able to: • Explain how Autoencoders and VAEs learn structured latent representations. • Analyze GAN training dynamics and diagnose instability issues such as mode collapse. • Compare advanced GAN architectures and evaluate output quality trade-offs. • Understand diffusion model fundamentals and reverse denoising processes. • Design U-Net-based diffusion systems for conditional image generation. • Implement text-conditioned diffusion with guided sampling techniques. • Apply mix