Generative and Adaptive Systems

Part IV

Deep models can do more than recognize patterns: they can learn distributions, construct new samples, and adapt behavior through feedback.

After the representation and pre-training systems of Part III, this Part turns from recognition toward generation and action. Chapter 11 introduces variational autoencoders and generative adversarial networks as contrasting approaches to learning data distributions. Chapter 12 develops diffusion models as iterative denoising systems whose probability structure enables high-quality, controllable generation. Chapter 13 shifts from data reconstruction to interaction, introducing reinforcement learning, value and policy methods, human feedback, and alignment.

The conceptual progression runs from latent representation, through probabilistic and adversarial generation, to agents that learn from consequences. Probability, iterative refinement, feedback, and objective design provide the common language.