Advanced Deep Learning with Keras

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References

Conclusion

Monte Carlo policy gradient (REINFORCE) method

Policy gradient theorem

Chapter 10. Policy Gradient Methods

References

Conclusion

Double Q-Learning (DDQN)

DQN on Keras

Deep Q-Network (DQN)

Q-Learning on OpenAI gym

Temporal-difference learning

Nondeterministic environment

Q-Learning in Python

Q-Learning example

The Q value

Principles of reinforcement learning (RL)

Chapter 9. Deep Reinforcement Learning

References

Conclusion

-VAE: VAE with disentangled latent representations

Conditional VAE (CVAE)

Principles of VAEs

Chapter 8. Variational Autoencoders (VAEs)

References

Conclusion

Generator outputs of CycleGAN

Implementing CycleGAN using Keras

The CycleGAN Model

Principles of CycleGAN

Chapter 7. Cross-Domain GANs

Reference

Conclusion

Generator outputs of StackedGAN

Implementation of StackedGAN in Keras

StackedGAN

Generator outputs of InfoGAN

Implementation of InfoGAN in Keras

InfoGAN

Disentangled representations

Chapter 6. Disentangled Representation GANs

References

Conclusion

Auxiliary classifier GAN (ACGAN)

Least-squares GAN (LSGAN)

Wasserstein GAN

Chapter 5. Improved GANs

References

Conclusion

Conditional GAN

GAN implementation in Keras

Principles of GANs

An overview of GANs

Chapter 4. Generative Adversarial Networks (GANs)

References

Conclusion

Automatic colorization autoencoder

Denoising autoencoder (DAE)

Building autoencoders using Keras

Principles of autoencoders

Chapter 3. Autoencoders

References

Conclusion

Densely connected convolutional networks (DenseNet)

ResNet v2

Deep residual networks (ResNet)

Functional API

Chapter 2. Deep Neural Networks

References

Conclusion

Recurrent neural networks (RNNs)

Convolutional neural networks (CNNs)

Multilayer perceptrons (MLPs)

Implementing the core deep learning models - MLPs CNNs and RNNs

Why is Keras the perfect deep learning library?

Chapter 1. Introducing Advanced Deep Learning with Keras

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To get the most out of this book

What this book covers

Who this book is for

Preface

Packt is Searching for Authors Like You

About the reviewer

About the author

Contributors

Packt.com

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www.mapt.io

Advanced Deep Learning with Keras

coverpage

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