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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