Advanced Deep Learning with Keras

Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more

Rowel Atienza

• Publisher: Packt Publishing
• Year: 2018
• ISBN:
  9781788624534
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

Understanding and coding advanced deep learning algorithms with the most intuitive deep learning library in existence

Key Features

• Explore the most advanced deep learning techniques that drive modern AI results
• Implement deep neural networks, autoencoders, GANs, VAEs, and deep reinforcement learning
• A wide study of GANs, including Improved GANs, Cross-Domain GANs, and Disentangled Representation GANs

Book Description

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.

What you will learn

• Cutting-edge techniques in human-like AI performance
• Implement advanced deep learning models using Keras
• The building blocks for advanced techniques - MLPs, CNNs, and RNNs
• Deep neural networks – ResNet and DenseNet
• Autoencoders and Variational Autoencoders (VAEs)
• Generative Adversarial Networks (GANs) and creative AI techniques
• Disentangled Representation GANs, and Cross-Domain GANs
• Deep reinforcement learning methods and implementation
• Produce industry-standard applications using OpenAI Gym
• Deep Q-Learning and Policy Gradient Methods

Who this book is for

Some fluency with Python is assumed. As an advanced book, you'll be familiar with some machine learning approaches, and some practical experience with DL will be helpful. Knowledge of Keras or TensorFlow 1.x is not required but would be helpful.

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Advanced Deep Learning with Keras

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Table of Contents

Advanced Deep Learning with Keras

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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

Download the example code files

Download the color images

Conventions used

Get in touch

Reviews

1. Introducing Advanced Deep Learning with Keras

Why is Keras the perfect deep learning library?

Installing Keras and TensorFlow

Implementing the core deep learning models - MLPs, CNNs, and RNNs

The difference between MLPs, CNNs, and RNNs

Multilayer perceptrons (MLPs)

MNIST dataset

MNIST digits classifier model

Building a model using MLPs and Keras

Regularization

Output activation and loss function

Optimization

Performance evaluation

Model summary

Convolutional neural networks (CNNs)

Convolution

Pooling operations

Performance evaluation and model summary

Recurrent neural networks (RNNs)

Conclusion

References

2. Deep Neural Networks

Functional API

Creating a two-input and one-output model

Deep residual networks (ResNet)

ResNet v2

Densely connected convolutional networks (DenseNet)

Building a 100-layer DenseNet-BC for CIFAR10

Conclusion

References

3. Autoencoders

Principles of autoencoders

Building autoencoders using Keras

Denoising autoencoder (DAE)

Automatic colorization autoencoder

Conclusion

References

4. Generative Adversarial Networks (GANs)

An overview of GANs

Principles of GANs

GAN implementation in Keras

Conditional GAN

Conclusion

References

5. Improved GANs

Wasserstein GAN

Distance functions

Distance function in GANs

Use of Wasserstein loss

WGAN implementation using Keras

Least-squares GAN (LSGAN)

Auxiliary classifier GAN (ACGAN)

Conclusion

References

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

7. Cross-Domain GANs

Principles of CycleGAN

The CycleGAN Model

Implementing CycleGAN using Keras

Generator outputs of CycleGAN

CycleGAN on MNIST and SVHN datasets

Conclusion

References

8. Variational Autoencoders (VAEs)

Principles of VAEs

Variational inference

Core equation

Optimization

Reparameterization trick

Decoder testing

VAEs in Keras

Using CNNs for VAEs

Conditional VAE (CVAE)

-VAE: VAE with disentangled latent representations

Conclusion

References

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

10. Policy Gradient Methods

Policy gradient theorem

Monte Carlo policy gradient (REINFORCE) method

REINFORCE with baseline method

Actor-Critic method

Advantage Actor-Critic (A2C) method

Policy Gradient methods with Keras

Performance evaluation of policy gradient methods

Conclusion

References

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Index

Advanced Deep Learning with Keras

Copyright © 2018 Packt Publishing

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Acquisition Editor: Frank Pohlmann, Andrew Waldron, Suresh Jain

Content Development Editor: Alex Sorrentino

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Indexers: Aishwarya Gangawane

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Production Coordinator: Sandip Tadge

First published: October 2018

Production reference: 1311018

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