Book Image

Advanced Deep Learning with Keras

By : Rowel Atienza
Book Image

Advanced Deep Learning with Keras

By: Rowel Atienza

Overview of this book

Recent developments in deep learning, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Deep Reinforcement Learning (DRL) are creating impressive AI results in our news headlines - such as AlphaGo Zero beating world chess champions, and generative AI that can create art paintings that sell for over $400k because they are so human-like. Advanced Deep Learning with Keras is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI. Using Keras as an open-source deep learning library, you'll find hands-on projects throughout that show you how to create more effective AI with the latest techniques. The journey begins with an overview of MLPs, CNNs, and RNNs, which are the building blocks for the more advanced techniques in the book. You’ll learn how to implement deep learning models with Keras and TensorFlow 1.x, and move forwards to advanced techniques, as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You then learn all about GANs, and how they can open new levels of AI performance. Next, you’ll get up to speed with how VAEs are implemented, and you’ll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans - a major stride forward for modern AI. To complete this set of advanced techniques, you'll learn how to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.

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Table of Contents (16 chapters)
Advanced Deep Learning with Keras
Advanced Deep Learning with Keras
Contributors
Contributors
Preface
Preface
Other Books You May Enjoy
Other Books You May Enjoy
Free Chapter
1
Introducing Advanced Deep Learning with Keras
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
2
Deep Neural Networks
Deep Neural Networks
Functional API
Deep residual networks (ResNet)
ResNet v2
Densely connected convolutional networks (DenseNet)
Conclusion
References
3
Autoencoders
Autoencoders
Principles of autoencoders
Building autoencoders using Keras
Denoising autoencoder (DAE)
Automatic colorization autoencoder
Conclusion
References
4
Generative Adversarial Networks (GANs)
Generative Adversarial Networks (GANs)
An overview of GANs
Principles of GANs
GAN implementation in Keras
Conditional GAN
Conclusion
References
5
Improved GANs
Improved GANs
Wasserstein GAN
Least-squares GAN (LSGAN)
Auxiliary classifier GAN (ACGAN)
Conclusion
References
6
Disentangled Representation GANs
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
7
Cross-Domain GANs
Cross-Domain GANs
Principles of CycleGAN
The CycleGAN Model
Implementing CycleGAN using Keras
Generator outputs of CycleGAN
Conclusion
References
8
Variational Autoencoders (VAEs)
Variational Autoencoders (VAEs)
Principles of VAEs
Conditional VAE (CVAE)
-VAE: VAE with disentangled latent representations
Conclusion
References
9
Deep Reinforcement Learning
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
10
Policy Gradient Methods
Policy Gradient Methods
Policy gradient theorem
Monte Carlo policy gradient (REINFORCE) method
Conclusion
References
Index
Index

Contributors

About the author

Rowel Atienza is an Associate Professor at the Electrical and Electronics Engineering Institute of the University of the Philippines, Diliman. He holds the Dado and Maria Banatao Institute Professorial Chair in Artificial Intelligence. Rowel has been fascinated with intelligent robots since he graduated from the University of the Philippines. He received his MEng from the National University of Singapore for his work on an AI-enhanced four-legged robot. He finished his Ph.D. at The Australian National University for his contribution on the field of active gaze tracking for human-robot interaction. Rowel's current research work focuses on AI and computer vision. He dreams on building useful machines that can perceive, understand, and reason. To help make his dreams become real, Rowel has been supported by grants from the Department of Science and Technology (DOST), Samsung Research Philippines, and Commission on Higher Education-Philippine California Advanced Research Institutes (CHED-PCARI).

I would like to thank my family, Che, Diwa, and Jacob. They never cease to support my work.

I would like to thank my mother who instilled into me the value of education.

I would like to express my gratitude to the people of Packt and this book's technical reviewer, Frank, Kishor, Alex, and Valerio. They are inspiring and easy to work with.

I would like to thank the institutions who always support my teaching and research agenda, University of the Philippines, DOST, Samsung Research PH, and CHED-PCARI.

I would like to acknowledge my students. They have been patient as I develop my courses in AI.

About the reviewer

Valerio Maggio is currently a Post-Doc Data Scientist at Fondazione Bruno Kessler (FBK) in Trento, Italy, responsible for Machine and Deep Learning in the MPBA lab (Predictive Models for Biomedicine and Environment). Valerio has a Ph.D. in Computational Science from the University of Naples "Federico II" His research interests are focused on Machine Learning and Deep Learning applied to Software Maintenance and Computational Biology. Valerio is very much involved in the scientific Python community, and he is an active speaker at many Python conference.

He is also the lead organiser of PyCon Italy/PyData Florence, and EuroSciPy. He uses Python as the mainstream language for his deep/machine learning code, making an intensive use of Python to analyse, visualise, and learn from data. In the context of Deep Learning, Valerio is the author of a quite popular Keras/TensorFlow tutorial, publicly available on his GitHub Profile github.com/leriomaggio/deep-learning-keras-tensorflow and presented in many conferences (EuroSciPy, PyData London, PySS) and University courses. Valerio is also passionate about (black) tea, and an "old-school" Magic The Gathering (MTG) player, who enjoys playing and teaching MTG to newbies.

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