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Introduction：
生成模型作为机器学习的一个子领域，在大量人工智能任务中得到了应用。近年来随着深度学习的突破，深度生成模型对人工智能各个子领域带来了大的改变，受到了学术界极大的关注，并在越来越多的应用中获得了突破。本课程将学习深度生成模型的基础学习算法，包括自回归模型、变分自编码器、生成对抗网络等。同时本课程将讲授深度生成模型在图像生成、计算机视觉、自然语言处理等方面的应用。最后，为了更深入地了解这个领域，我们将讲解前沿的生成模型技术和当前遇到的一些技术挑战，并代领大家阅读一些重要论文和完成一个课程项目。
README.md
深度生成模型
北京大学 董豪 hao.dong@pku.edu.cn
基础
| Week 1 | Introduction | Lecture 1: Introduction Lecture 2: Data Representation Lecture 3: Mathematic Foundation & Basic Concept |
| Week 2 | Autoregressive Models | Lecture 4: Sequential Models - Recurrent Neural Networks Lecture 5: Autoregressive Models 1 Lecture 6: Autoregressive Models 2 |
| Week 3 | Variational Autoencoders | Lecture 7: From Autoencoder to VAE Lecture 8: Variational Autoencoder Lecture 9: VAE Variants |
| Week 4 | Normalising Flow Models | Lecture 10: Normalising Flow Background Lecture 11-12: Normalising Flow Models |
| Week 5 | Generative Adversarial Networks | Lecture 13: Introduction of GAN Lecture 14: Understanding GAN Lecture 15: Selected GANs |
| Week 6 | Practice | Lecture 16-18: Practice: VAE and GAN Lecture 16-18: Demo Code |
研究 & 应用
| Week 7 | Evaluation of Generative Models | Lecture 19: Sampling Quality Lecture 20: Density Evaluation & Latent Representation Lecture 21: Practice |
| Week 8 | Energy-based Models | Lecture 22: Hopfield Network Lecture 23: Boltzmann Machine Lecture 24: Energy-based GANs |
| Week 9 | Challenges of Generative Models | Lecture 25: High-dimensional Data Generation Lecture 26: Learning Large Encoder Lecture 27: Other Challenges |
| Week 10 | Applications of Generative Models | Lecture 28: Image Synthesis, Translation and Manipulation Lecture 29: X Learning Lecture 30: Advanced Topics |
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