Packt.Advanced.Deep.Learning.with.Keras.1788629418.pdf

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Title Page

About the author

Acknowledgments

About the reviewer

Table of Contents

Preface

1 Introducing Advanced Deep Learning with Keras

2 Deep Neural Networks

3 Autoencoders

4 Generative Adversarial Networks (GANs)

5 Improved GANs

6 Disentangled Representation GANs

7 Cross-Domain GANs

8 Variational Autoencoders (VAEs)

9 Deep Reinforcement Learning

10 Policy Gradient Methods

Other Books You May Enjoy

Index

Advanced Deep Learning with Keras Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more Rowel Atienza BIRMINGHAM - MUMBAI

Advanced Deep Learning with Keras Copyright © 2018 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. Acquisition Editor: Frank Pohlmann, Andrew Waldron, Suresh Jain Content Development Editor: Alex Sorrentino Technical Editor: Gaurav Gavas Project Editor: Kishor Rit Proofreader: Safis Editing Indexers: Aishwarya Gangawane Graphics: Tom Scaria Production Coordinator: Sandip Tadge First published: October 2018 Production reference: 1311018 Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK. ISBN 978-1-78862-941-6 www.packtpub.com

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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 Learning 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. Packt is Searching for Authors Like You If you're interested in becoming an author for Packt, please visit authors.packtpub. com and apply today. We have worked with thousands of developers and tech professionals, just like you, to help them share their insight with the global tech community. You can make a general application, apply for a specific hot topic that we are recruiting an author for, or submit your own idea.

Table of Contents Preface Chapter 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 Chapter 2: Deep Neural Networks Functional API Creating a two-input and one-output model V 1 2 3 4 5 6 6 8 12 14 15 17 20 21 23 26 27 28 31 37 39 40 43 [ i ]

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