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醉染图书生成式深度学习()9787564188276
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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 Ehtion
Autoencoders
Your First Autoencoder
The Encoder
The Decoder
Joining the Encoder to the Decoder
Analysis of the Autoencoder
The Variational Art Ehtion
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
……
大卫·福斯特是Applied data Science公司的联合创始人,这是一家为客户提供创新解决方案的数据科学咨询公司。他拥有英国剑桥大学三一学院数学硕士和华威大学运筹学硕士。
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