book

Generative Deep Learning

by David Foster

June 2019

Intermediate to advanced

327 pages

7h 36m

English

O'Reilly Media, Inc.

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Objective and ApproachPrerequisitesOther ResourcesConventions Used in This BookUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgments
What Is Generative Modeling?Generative Versus Discriminative ModelingAdvances in Machine LearningThe Rise of Generative ModelingThe Generative Modeling FrameworkProbabilistic Generative ModelsHello Wrodl!Your First Probabilistic Generative ModelNaive BayesHello Wrodl! ContinuedThe Challenges of Generative ModelingRepresentation LearningSetting Up Your EnvironmentSummary
Structured and Unstructured DataDeep Neural NetworksKeras and TensorFlowYour First Deep Neural NetworkLoading the DataBuilding the ModelCompiling the ModelTraining the ModelEvaluating the ModelImproving the ModelConvolutional LayersBatch NormalizationDropout LayersPutting It All TogetherSummary
The Art ExhibitionAutoencodersYour First AutoencoderThe EncoderThe DecoderJoining the Encoder to the DecoderAnalysis of the AutoencoderThe Variational Art ExhibitionBuilding a Variational AutoencoderThe EncoderThe Loss FunctionAnalysis of the Variational AutoencoderUsing VAEs to Generate FacesTraining the VAEAnalysis of the VAEGenerating New FacesLatent Space ArithmeticMorphing Between FacesSummary
GanimalsIntroduction to GANsYour First GANThe DiscriminatorThe GeneratorTraining the GANGAN ChallengesOscillating LossMode CollapseUninformative LossHyperparametersTackling the GAN ChallengesWasserstein GANWasserstein LossThe Lipschitz ConstraintWeight ClippingTraining the WGANAnalysis of the WGANWGAN-GPThe Gradient Penalty LossAnalysis of WGAN-GPSummary
Apples and OrgangesCycleGANYour First CycleGANOverviewThe Generators (U-Net)The DiscriminatorsCompiling the CycleGANTraining the CycleGANAnalysis of the CycleGANCreating a CycleGAN to Paint Like MonetThe Generators (ResNet)Analysis of the CycleGANNeural Style TransferContent LossStyle LossTotal Variance LossRunning the Neural Style TransferAnalysis of the Neural Style Transfer ModelSummary
The Literary Society for Troublesome MiscreantsLong Short-Term Memory NetworksYour First LSTM NetworkTokenizationBuilding the DatasetThe LSTM ArchitectureThe Embedding LayerThe LSTM LayerThe LSTM CellGenerating New TextRNN ExtensionsStacked Recurrent NetworksGated Recurrent UnitsBidirectional CellsEncoder–Decoder ModelsA Question and Answer GeneratorA Question-Answer DatasetModel ArchitectureInferenceModel ResultsSummary
PreliminariesMusical NotationYour First Music-Generating RNNAttentionBuilding an Attention Mechanism in KerasAnalysis of the RNN with AttentionAttention in Encoder–Decoder NetworksGenerating Polyphonic MusicThe Musical OrganYour First MuseGANThe MuseGAN GeneratorChords, Style, Melody, and GrooveThe Bar GeneratorPutting It All TogetherThe CriticAnalysis of the MuseGANSummary

Reinforcement LearningOpenAI GymWorld Model ArchitectureThe Variational AutoencoderThe MDN-RNNThe ControllerSetupTraining Process OverviewCollecting Random Rollout DataTraining the VAEThe VAE ArchitectureExploring the VAECollecting Data to Train the RNNTraining the MDN-RNNThe MDN-RNN ArchitectureSampling the Next z and Reward from the MDN-RNNThe MDN-RNN Loss FunctionTraining the ControllerThe Controller ArchitectureCMA-ESParallelizing CMA-ESOutput from the Controller TrainingIn-Dream TrainingIn-Dream Training the ControllerChallenges of In-Dream TrainingSummary
Five Years of ProgressThe TransformerPositional EncodingMultihead AttentionThe DecoderAnalysis of the TransformerBERTGPT-2MuseNetAdvances in Image GenerationProGANSelf-Attention GAN (SAGAN)BigGANStyleGANApplications of Generative ModelingAI ArtAI Music

Overview

Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models, and world models.

Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative.

Discover how variational autoencoders can change facial expressions in photos
Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation
Create recurrent generative models for text generation and learn how to improve the models using attention
Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting
Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN