Rowel Atienza - Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more, 2nd Edition
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- Book:Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more, 2nd Edition
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Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and Keras
Key Features- Explore the most advanced deep learning techniques that drive modern AI results
- New coverage of unsupervised deep learning using mutual information, object detection, and semantic segmentation
- Completely updated for TensorFlow 2.x
Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects.
Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques.
Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance.
Next, youll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. Youll also learn 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- Use mutual information maximization techniques to perform unsupervised learning
- Use segmentation to identify the pixel-wise class of each object in an image
- Identify both the bounding box and class of objects in an image using object detection
- Learn the building blocks for advanced techniques - MLPss, CNN, and RNNs
- Understand deep neural networks - including ResNet and DenseNet
- Understand and build autoregressive models autoencoders, VAEs, and GANs
- Discover and implement deep reinforcement learning methods
This is not an introductory book, so fluency with Python is required. The reader should also be familiar with some machine learning approaches, and practical experience with DL will also be helpful. Knowledge of Keras or TensorFlow 2.0 is not required but is recommended.
Table of Contents- Introducing Advanced Deep Learning with Keras
- Deep Neural Networks
- Autoencoders
- Generative Adversarial Networks (GANs)
- Improved GANs
- Disentangled Representation GANs
- Cross-Domain GANs
- Variational Autoencoders (VAEs)
- Deep Reinforcement Learning
- Policy Gradient Methods
- Object Detection
- Semantic Segmentation
- Unsupervised Learning Using Mutual Information
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Advanced Deep Learning with TensorFlow 2 and Keras
Second Edition
Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more
Rowel Atienza
BIRMINGHAM - MUMBAI
Advanced Deep Learning with TensorFlow 2 and Keras
Second Edition
Copyright 2020 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.
Producer: Andrew Waldron
Project Editor: Janice Gonsalves
Content Development Editor: Dr. Ian Hough
Technical Editor: Karan Sonawane
Copy Editor: Safis Editing
Proofreader: Safis Editing
Indexer: Pratik Shirodkar
Production Designer: Sandip Tadge
First published: October 2018
Second edition: February 2020
Production reference: 1260220
Published by Packt Publishing Ltd.
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Birmingham B3 2PB, UK.
ISBN 978-1-83882-165-4
www.packt.com
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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 and received his MEng from the National University of Singapore for his work on an AI-enhanced four-legged robot. He gained his Ph.D. at The Australian National University for his contribution in the field of active gaze tracking for human-robot interaction. His current research work focuses on AI and computer vision.
I would like to thank my family, Cherry, 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, Janice, Ian, Karan, 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.
Valerio Maggio received his Ph.D in Computational Science by the Dept. of Mathematics of the University of Naples "Federico II", with a thesis in machine learning and software engineering entitled "Improving Software Maintenance using Unsupervised Machine Learning techniques." After some years as a postdoc researcher and lecturer at the University of Salerno and at the University of Basilicata, he joined the "Predictive Models for Biomedicine and Environment" lab at Fondazione Bruno Kessler (FBK), where he worked as a Research Associate. Valerio is currently a Senior Research Associate at the Dynamic Genetics Lab at University of Bristol ( http://dynamicgenetics.org/ ). His research interests focus on methods and software for reproducible machine learning and deep learning for biomedicine. Valerio is also a Cloud Research Software Engineer as part of the Microsoft initiative for Higher Education and Research, and a very active member of the Python community. He is a lead member of the organising committee of many international conferences, such as EuroPython, PyCon/PyData Italy, and EuroScipy.
In recent years, Deep Learning has made unprecedented success stories in difficult problems in vision, speech, natural language processing and understanding, and all other areas with abundance of data. The interest in this field from companies, universities, governments, and research organizations has accelerated the advances in the field. This book covers select important topics in Deep Learning with three new chapters, Object Detection, Semantic Segmentation, and Unsupervised Learning using Mutual Information. The advanced theories are explained by giving a background of the principles, digging into the intuition behind the concepts, implementing the equations and algorithms using Keras, and examining the results.
Artificial Intelligence (AI), as it stands today, is still far from being a well-understood field. Deep Learning (DL), as a sub field of AI, is in the same position. While it is far from being a mature field, many real-world applications such as vision-based detection and recognition, autonomous navigation, product recommendation, speech recognition and synthesis, energy conservation, drug discovery, finance, and marketing are already using DL algorithms. Many more applications will be discovered and built. The aim of this book is to explain advanced concepts, give sample implementations, and let the readers as experts in their field identify the target applications.
A field that is not completely mature is a double-edged sword. On one edge, it offers a lot of opportunities for discovery and exploitation. There are many unsolved problems in deep learning. This translates into opportunities to be the first to market be that in product development, publication, or recognition. The other edge is it would be difficult to trust a not-fully-understood field in a mission-critical environment. We can safely say that if asked, very few machine learning engineers will ride an auto-pilot plane controlled by a deep learning system. There is a lot of work to be done to gain this level of trust. The advanced concepts that are discussed in this book have a high chance of playing a major role as the foundation in gaining this level of trust.
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