更新时间：2021-07-02 16:21:17
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Advanced Deep Learning with Keras
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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)
Chapter 3. Autoencoders
Principles of autoencoders
Building autoencoders using Keras
Denoising autoencoder (DAE)
Automatic colorization autoencoder
Chapter 4. Generative Adversarial Networks (GANs)
An overview of GANs
Principles of GANs
GAN implementation in Keras
Conditional GAN
Chapter 5. Improved GANs
Wasserstein GAN
Least-squares GAN (LSGAN)
Auxiliary classifier GAN (ACGAN)
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
Reference
Chapter 7. Cross-Domain GANs
Principles of CycleGAN
The CycleGAN Model
Implementing CycleGAN using Keras
Generator outputs of CycleGAN
Chapter 8. Variational Autoencoders (VAEs)
Principles of VAEs
Conditional VAE (CVAE)
-VAE: VAE with disentangled latent representations
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)
Chapter 10. Policy Gradient Methods
Policy gradient theorem
Monte Carlo policy gradient (REINFORCE) method
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