生成式建模（generative modeling）是人工智能领域热门的研究课题之一。现在算法已经可以教一台机器在绘画、写作和作曲等人类活动中取得出色的表现。通过这本实用指南，机器学习工程师和数据科学家们将学会如何通过生成式深度学习模型重新创建一些令人印象深刻的程序示例，例如变分自编码器、生成对抗网络、编码器一解码器模型和世界模型。作者David Foster在书中演示了每种技术的内部工作原理，首先介绍了使用Keras进行深度学习的基本知识，然后介绍了该领域先进的一些算法。通过书中的提示和技巧，你将了解如何使模型更有效地学习并变得更有创造性。
探索变分自编码器如何改变照片中的人脸表情从头开始构建实用的GAN示例，包括用于样式转换的CycleGAN和用于音乐生成的MuseGAN算法创建循环生成式模型实现文本生成，并学习如何使用注意力改进模型了解生成式模型如何借助并行代理在强化学习环境中完成任务探索Transformer（BERT，GPT-2）模型架构以及ProGAN和StyleGAN等图像生成模型
Preface
Part Ⅰ Introduction to Generative Deep Learning
1． Generative Modeling
What Is Generative Modeling?
Generative Versus Discriminative Modeling
Advances in Machine Learning
The Rise of Generative Modeling
The Generative Modeling Framework
Probabilistic Generative Models
Hello Wrodl！
Your First Probabilistic Generative Model
Naive Bayes
Hello Wrodl！ Continued
The Challenges of Generative Modeling
Representation Learning
Setting Up Your Environment
Summary
2． Deep Learning
Structured and Unstructured Data
Deep Neural Networks
Keras and TensorFlow
Your First Deep Neural Network
Loading the Data
Building the Model
Compiling the Model
Training the Model
Evaluating the Model
Improving the Model
Convolutional Layers
Batch Normalization
Dropout Layers
Putting It All Together
Summary
3． Variational Autoencoflers
The Art Exhibition
Autoencoders
Your First Autoencoder
The Encoder
The Decoder
Joining the Encoder to the Decoder
Analysis of the Autoencoder
The Variational Art Exhibition
Building a Variational Autoencoder
The Encoder
The Loss Function
Analysis of the Variational Autoencoder
Using VAEs to Generate Faces
Training the VAE
Analysis of the VAE
Generating New Faces
……
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