作者简介 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学位和华威大学运筹学硕士学位。
目录 Preface Part Ⅰ I troductio to Ge erative Deep Lear i g 1. Ge erative Modeli g What Is Ge erative Modeli g Ge erative Versus Discrimi ative Modeli g Adva ces i Machi e Lear i g The Rise of Ge erative Modeli g The Ge erative Modeli g Framework Probabilistic Ge erative Models Hello Wrodl! Your First Probabilistic Ge erative Model aive Bayes Hello Wrodl! Co ti ued The Challe ges of Ge erative Modeli g Represe tatio Lear i g Setti g Up Your E viro me t Summary 2. Deep Lear i g Structured a d U structured Data Deep eural etworks Keras a d Te sorFlow Your First Deep eural etwork Loadi g the Data Buildi g the Model Compili g the Model Trai i g the Model Evaluati g the Model Improvi g the Model Co volutio al Layers Batch ormalizatio Dropout Layers Putti g It All Together Summary 3. Variatio al Autoe coflers The Art Exhibitio Autoe coders Your First Autoe coder The E coder The Decoder Joi i g the E coder to the Decoder A alysis of the Autoe coder The Variatio al Art Exhibitio Buildi g a Variatio al Autoe coder The E coder The Loss Fu ctio A alysis of the Variatio al Autoe coder Usi g VAEs to Ge erate Faces Trai i g the VAE A alysis of the VAE Ge erati g ew Faces
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