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Gulli Antonio - Deep Learning with Keras: Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games

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Get to grips with the basics of Keras to implement fast and efficient deep-learning models About This Book Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games See how various deep-learning models and practical use-cases can be implemented using Keras A practical, hands-on guide with real-world examples to give you a strong foundation in Keras Who This Book Is For If you are a data scientist with experience in machine learning or an AI programmer with some exposure to neural networks, you will find this book a useful entry point to deep-learning with Keras. A knowledge of Python is required for this book. What You Will Learn Optimize step-by-step functions on a large neural network using the Backpropagation Algorithm Fine-tune a neural network to improve the quality of results Use deep learning for image and audio processing Use Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases Identify problems for which Recurrent Neural Network (RNN) solutions are suitable Explore the process required to implement Autoencoders Evolve a deep neural network using reinforcement learning In Detail This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of hand written digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GAN). You will also explore non-traditional uses of neural networks as Style Transfer. Finally, you will look at Reinforcement Learning and its application to AI game playing, another popular direction of research and application of neural networks. Style and approach This book is an easy-to-follow guide full of examples and real-world applications to help you gain an in-depth understanding of Keras. This book will showcase more than twenty working Deep Neural Networks coded in Python using Keras. Downloading the example code for thi ...

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Deep Learning with Keras Implementing deep learning models and neural networks - photo 1
Deep Learning with Keras
Implementing deep learning models and neural networks with the power of Python
Antonio Gulli
Sujit Pal
BIRMINGHAM - MUMBAI Deep Learning with Keras Copyright 2017 Packt Publishing - photo 2

BIRMINGHAM - MUMBAI

Deep Learning with Keras

Copyright 2017 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 authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be 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.

First published: April 2017

Production reference: 1240417

Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham
B3 2PB, UK.

ISBN 978-1-78712-842-2

www.packtpub.com

Credits Authors Antonio Gulli Sujit Pal Copy Editor Vikrant Phadkay - photo 3

Credits

Authors
Antonio Gulli
Sujit Pal

Copy Editor
Vikrant Phadkay

Reviewers
Mike Dahlin
Nick McClure
Corrado Zocollo

Project Coordinator
Nidhi Joshi

Commissioning Editor
Amey Varangaonkar

Proofreader
Safis Editing

Acquisition Editor
Divya Poojari

Indexer
Francy Puthiry

Content Development Editor
Cheryl Dsa

Graphics
Tania Dutta

Technical Editor
Dinesh Pawar

Production Coordinator
Arvindkumar Gupta

About the Authors

Antonio Gulli is a software executive and business leader with a passion for establishing and managing global technological talent, innovation, and execution. He is an expert in search engines, online services, machine learning, information retrieval, analytics, and cloud computing. So far, he has been lucky enough to gain professional experience in four different countries in Europe and managed people in six different countries in Europe and America. Antonio served as CEO, GM, CTO, VP, director, and site lead in multiple fields spanning from publishing (Elsevier) to consumer internet (Ask.com and Tiscali) and high-tech R&D (Microsoft and Google).

I would like to thank my coauthor, Sujit Pal, for being a such talented colleague, always willing to help with a humble spirit. I constantly appreciate his dedication to teamwork, which made this book a real thing.
I would like to thank Francois Chollet (and the many Keras contributors) for taking the time and effort to build an awesome deep learning toolkit that is easy to use without sacrificing too much power.
I would also like to thank our editors from Packt, Divya Poojari, Cheryl Dsa, and Dinesh Pawar, and our reviewers from Packt and Google, for their support and valuable suggestions. This book would not have been possible without you.
I would like to thank my manager, Brad, and my colleagues Mike and Corrado at Google for encouraging me to write this book, and for their constant help in reviewing the content.
I would like to thank Same Fusy, Herbaciarnia i Kawiarnia in Warsaw. I got the initial inspiration to write this book in front of a cup of tea chosen among hundreds of different offers. This place is magic and I strongly recommend visiting it if you are in search of a place to stimulate creativeness (http://www.samefusy.pl/).
Then I would like to thank HRBP at Google for supporting my wish to donate all of this book's royalties in favor of a minority/diversity scholarship.
I would like to thank my friends Eric, Laura, Francesco, Ettore, and Antonella for supporting me when I was in need. Long-term friendship is a real thing, and you are true friends to me.
I would like to thank my son Lorenzo for encouraging me to join Google, my son Leonardo for his constant passion to discover new things, and my daughter Aurora for making me smile every day of my life. Finally thanks to my father Elio and my mother Maria for their love.

Sujit Pal is a technology research director at Elsevier Labs, working on building intelligent systems around research content and metadata. His primary interests are information retrieval, ontologies, natural language processing, machine learning, and distributed processing. He is currently working on image classification and similarity using deep learning models. Prior to this, he worked in the consumer healthcare industry, where he helped build ontology-backed semantic search, contextual advertising, and EMR data processing platforms. He writes about technology on his blog at Salmon Run.

I would like to thank my coauthor, Antonio Gulli, for asking me to join him in writing this book. This was an incredible opportunity and a great learning experience for me. Besides, had he not done so, I quite literally wouldn't have been here today.
I would like to thank Ron Daniel, the director of Elsevier Labs, and Bradley P Allen, chief architect at Elsevier, for introducing me to deep learning and making me a believer in its capabilities.
I would also like to thank Francois Chollet (and the many Keras contributors) for taking the time and effort to build an awesome deep learning toolkit that is easy to use without sacrificing too much power.
Thanks to our editors from Packt, Divya Poojari, Cheryl Dsa, and Dinesh Pawar, and our reviewers from Packt and Google, for their support and valuable suggestions. This book would not have been possible without you.
I would like to thank my colleagues and managers over the years, especially the ones who took their chances with me and helped me make discontinuous changes in my career.
Finally, I would like to thank my family for putting up with me these past few months as I juggled work, this book, and family, in that order. I hope you will agree that it was all worth it.
About the Reviewer

Nick McClure is currently a senior data scientist at PayScale Inc. in Seattle, Washington, USA. Prior to that, he worked at Zillow and Caesars Entertainment. He got his degrees in applied mathematics from the University of Montana and the College of Saint Benedict and Saint John's University. Nick has also authored TensorFlow Machine Learning Cookbook by Packt Publishing.

He has a passion for learning and advocating for analytics, machine learning, and artificial intelligence. Nick occasionally puts his thoughts and musing on his blog, fromdata.org, or through his Twitter account at @nfmcclure.

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