Advanced Deep Learning with Keras

Chapter 5, Improved GANs, covers algorithms that improve the basic GAN. The
algorithms address the difficulty in training GANs and improve the perceptual
quality of synthetic data. WGAN, LSGAN, and ACGAN are discussed and
implemented in Keras.
Preface
Chapter 6, Disentangled Representation GANs, discusses how to control the attributes
of the synthetic data generated by GANs. The attributes can be controlled if the latent
representations are disentangled. Two techniques in disentangling representations,
InfoGAN and StackedGAN, are covered and implemented in Keras.
Chapter 7, Cross-Domain GANs, covers a practical application of GANs, translating
images from one domain to another or commonly known as cross-domain transfer.
CycleGAN, a widely used cross-domain GAN, is discussed and implemented in
Keras. This chapter also demonstrates CycleGAN performing colorization and
style transfer.
Chapter 8, Variational Autoencoders (VAEs), discusses another recent significant
advance in deep learning. Similar to GAN, VAE is a generative model that is
used to produce synthetic data. Unlike GAN, VAE focuses on decodable continuous
latent space that is suitable for variational inference. VAE and its variations,
CVAE and β -VAE, are covered and implemented in Keras.
Chapter 9, Deep Reinforcement Learning, explains the principles of reinforcement
learning and Q-Learning. Two techniques in implementing Q-Learning for
discrete action spaces are presented, Q Table update and Deep Q Network (DQN).
Implementation of Q-Learning using Python and DQN in Keras are demonstrated
in OpenAI gym environments.
Chapter 10, Policy Gradient Methods, explains how to use neural networks to learn the
policy for decision making in reinforcement learning. Four methods are covered and
implemented in Keras and OpenAI gym environment, REINFORCE, REINFORCE
with Baseline, Actor-Critic, and Advantage Actor-Critic. The example presented in
this chapter demonstrates policy gradient methods on a continuous action space.
To get the most out of this book
• Deep learning and Python: The reader should have a fundamental
knowledge of deep learning and its implementation in Python. While
previous experience in using Keras to implement deep learning algorithms
is important, it is not required. Chapter 1, Introducing Advanced Deep Learning
with Keras offers a review of deep learning concepts and their implementation
in Keras.
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