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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
……
