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Advanced Deep Learning with Keras
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Index
Recentdevelopmentsindeeplearning,includingGANs,VariationalAutoencoders,andDeepReinforcementLearning,arecreatingimpressiveAIresultsinournewsheadlines-suchasAlphaGoZerobeatingworldchesschampions,andgenerativeAIthatcancreateartpaintingsthatsellforover$400kbecausetheyaresohuman-like.AdvancedDeepLearningwithKerasisacomprehensiveguidetotheadvanceddeeplearningtechniquesavailabletoday,soyoucancreateyourowncutting-edgeAI.UsingKerasasanopen-sourcedeeplearninglibrary,you'llfindhands-onprojectsthroughoutthatshowyouhowtocreatemoreeffectiveAIwiththelatesttechniques.ThejourneybeginswithanoverviewofMLPs,CNNs,andRNNs,whicharethebuildingblocksforthemoreadvancedtechniquesinthebook.You’lllearnhowtoimplementdeeplearningmodelswithKerasandTensorflow,andmoveforwardstoadvancedtechniques,asyouexploredeepneuralnetworkarchitectures,includingResNetandDenseNet,andhowtocreateAutoencoders.YouthenlearnallaboutGenerativeAdversarialNetworks(GANs),andhowtheycanopennewlevelsofAIperformance.VariationalAutoEncoders(VAEs)areimplemented,andyou’llseehowGANsandVAEshavethegenerativepowertosynthesizedatathatcanbeextremelyconvincingtohumans-amajorstrideforwardformodernAI.Tocompletethissetofadvancedtechniques,you'lllearnhowtoimplementDeepReinforcementLearning(DRL)suchasDeepQ-LearningandPolicyGradientMethods,whicharecriticaltomanymodernresultsinAI.
目錄(93章)
倒序
- coverpage
- Advanced Deep Learning with Keras
- www.mapt.io
- Why subscribe?
- Packt.com
- Contributors
- About the author
- About the reviewer
- Packt is Searching for Authors Like You
- Preface
- Who this book is for
- What this book covers
- To get the most out of this book
- Get in touch
- Chapter 1. Introducing Advanced Deep Learning with Keras
- Why is Keras the perfect deep learning library?
- Implementing the core deep learning models - MLPs CNNs and RNNs
- Multilayer perceptrons (MLPs)
- Convolutional neural networks (CNNs)
- Recurrent neural networks (RNNs)
- Conclusion
- References
- Chapter 2. Deep Neural Networks
- Functional API
- Deep residual networks (ResNet)
- ResNet v2
- Densely connected convolutional networks (DenseNet)
- Conclusion
- References
- Chapter 3. Autoencoders
- Principles of autoencoders
- Building autoencoders using Keras
- Denoising autoencoder (DAE)
- Automatic colorization autoencoder
- Conclusion
- References
- Chapter 4. Generative Adversarial Networks (GANs)
- An overview of GANs
- Principles of GANs
- GAN implementation in Keras
- Conditional GAN
- Conclusion
- References
- Chapter 5. Improved GANs
- Wasserstein GAN
- Least-squares GAN (LSGAN)
- Auxiliary classifier GAN (ACGAN)
- Conclusion
- References
- Chapter 6. Disentangled Representation GANs
- Disentangled representations
- InfoGAN
- Implementation of InfoGAN in Keras
- Generator outputs of InfoGAN
- StackedGAN
- Implementation of StackedGAN in Keras
- Generator outputs of StackedGAN
- Conclusion
- Reference
- Chapter 7. Cross-Domain GANs
- Principles of CycleGAN
- The CycleGAN Model
- Implementing CycleGAN using Keras
- Generator outputs of CycleGAN
- Conclusion
- References
- Chapter 8. Variational Autoencoders (VAEs)
- Principles of VAEs
- Conditional VAE (CVAE)
- -VAE: VAE with disentangled latent representations
- Conclusion
- References
- Chapter 9. Deep Reinforcement Learning
- Principles of reinforcement learning (RL)
- The Q value
- Q-Learning example
- Q-Learning in Python
- Nondeterministic environment
- Temporal-difference learning
- Q-Learning on OpenAI gym
- Deep Q-Network (DQN)
- DQN on Keras
- Double Q-Learning (DDQN)
- Conclusion
- References
- Chapter 10. Policy Gradient Methods
- Policy gradient theorem
- Monte Carlo policy gradient (REINFORCE) method
- Conclusion
- References
- Other Books You May Enjoy
- Leave a review - let other readers know what you think
- Index 更新時間：2021-07-02 16:21:17
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