生成式建模（generative modeling）是人工智能領域熱門的研究課題之一�，F在算法已經可以教一臺機器在繪畫、寫作和作曲等人類活動中取得出色的表現。通過這本實用指南，機器學習工程師和數據科學家們將學會如何通過生成式深度學習模型重新創(chuàng)建一些令人印象深刻的程序示例，例如變分自編碼器、生成對抗網絡、編碼器一解碼器模型和世界模型。作者David Foster在書中演示了每種技術的內部工作原理，首先介紹了使用Keras進行深度學習的基本知識，然后介紹了該領域先進的一些算法。通過書中的提示和技巧，你將了解如何使模型更有效地學習并變得更有創(chuàng)造性。
探索變分自編碼器如何改變照片中的人臉表情從頭開始構建實用的GAN示例，包括用于樣式轉換的CycleGAN和用于音樂生成的MuseGAN算法創(chuàng)建循環(huán)生成式模型實現文本生成，并學習如何使用注意力改進模型了解生成式模型如何借助并行代理在強化學習環(huán)境中完成任務探索Transformer（BERT，GPT-2）模型架構以及ProGAN和StyleGAN等圖像生成模型
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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