目录 Foreword Preface Part I. Introduction to Generative Deep Learning 1. Generative Modeling 2. Deep Learning Part II. Methods 3. Variational Autoencoders 4. Generative Adversarial Networks 5. Autoregressive Models 6. Normalizing Flow Models 7. Energy-Based Models 8. Diffusion Models Part III. Applications 9. Transformers 10. Advanced GANs 11. Music Generation 12. World Models
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