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Bernico - Deep learning quick reference : useful hacks for training and optimizing deep neural networks with TensorFlow and Keras

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Bernico Deep learning quick reference : useful hacks for training and optimizing deep neural networks with TensorFlow and Keras
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Deep learning has become an essential necessity to enter the world of artificial intelligence. With this book, deep learning techniques will become more accessible, practical, and relevant to practicing data scientists. It moves deep learning from academia to the real world through practical examples. You will learn how Tensor Board is used to monitor the training of deep neural networks and solve binary classification problems using deep learning. Readers will then learn to optimize hyperparameters in their deep learning models. The book then takes the readers through the practical implementation of training CNNs, RNNs, and LSTMs with word embeddings and seq2seq models from scratch. Later, the book explores advanced topics such as Deep Q Network to solve an autonomous agent problem and how to use two adversarial networks to generate artificial images that appear real. For implementation purposes, we look at popular Python-based deep learning frameworks such as Keras and Tensorflow, Each chapter provides best practices and safe choices to help readers make the right decision while training deep neural networks--Cover, page 4. Read more...

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Deep Learning Quick Reference Useful hacks for training and optimizing deep - photo 1
Deep Learning Quick Reference
Useful hacks for training and optimizing deep neural networks with TensorFlow and Keras
Mike Bernico

BIRMINGHAM - MUMBAI Deep Learning Quick Reference Copyright 2018 Packt - photo 2

BIRMINGHAM - MUMBAI
Deep Learning Quick Reference

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.

Commissioning Editor: Amey Varangaonkar
Acquisition Editor: Viraj Madhav
Content Development Editor: Varun Sony
Technical Editor: Dharmendra Yadav
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Production Coordinator: Deepika Naik

First published: March 2018

Production reference: 1070318

Published by Packt Publishing Ltd.
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B3 2PB, UK.

ISBN 978-1-78883-799-6

www.packtpub.com


To my wife, Lana, whose love and support define the best epoch of my life
To my son, William, who is likely disappointed that this book doesn't have more dragons in it
To my mother, Sharon, and to the memory of my father, Bob, who taught me that determination and resilience matter more than intelligence
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Foreword

I first met Mike Bernico when we were two of the founding members of a new data science team at a Fortune 50 company. Then, it was a heady time; there wasn't such a thing as formal data science education, so we were all self-taught. We were a collection of adventurous people from diverse backgrounds, who identified and learned data science techniques because we needed them to solve the problems that we were interested in. We built a team with an optimistic hacker approachthe belief that we could find and apply techniques "from the wild" to build interesting, useful things.

It is in this practical, scrappy spirit that Mike wrote Deep Learning Quick Reference book. Deep learning is frequently made out to be mysterious and difficult; however, in this guide, Mike breaks down major deep learning techniques, making them approachable and applicable. With this book, you (yes, you!) can quickly get started with using deep learning for your own projects in a variety of different modalities.

Mike has been practising data science since before the discipline was named, and he has been specifically teaching the topic to university students for 3 years. Prior to this, he spent many years as a working computer scientist with a specialization in networks and security, and he also has a knack for engaging with people and communicating with nonspecialists. He is currently the Lead Data Scientist for a large financial services company, where he designs systems for data science, builds machine learning models with direct applications and for research publications, mentors junior data scientists, and teaches stakeholders about data science. He knows his stuff!

With Deep Learning Quick Reference book, you'll benefit from Mike's deep experience, humor, and down-to-earth manner as you build example networks alongside him. After you complete Mike's book, you'll have the confidence and knowledge to understand and apply deep learning to the problems of your own devising, for both fun and function.

Bon voyage, and good hacking!

- J. Malia Andrus, Ph.D.

Data Scientist
Seattle Washington

Contributors
About the author

Mike Bernico is a Lead Data Scientist at State Farm Mutual Insurance Companies. He also works as an adjunct for the University of Illinois at Springfield, where he teaches Essentials of Data Science, and Advanced Neural Networks and Deep Learning. Mike earned his MSCS from the University of Illinois at Springfield. He's an advocate for open source software and the good it can bring to the world. As a lifelong learner with umpteen hobbies, Mike also enjoys cycling, travel photography, and wine making.

I'd like to thank the very talented State Farm Data Scientists, current and past, for their friendship, expertise, and encouragement.
Thanks to my technical reviewers for providing insight and assistance with this book.
Most importantly, Id like to thank my wife, Lana, and my son, Will, for making time for this in our lives.
About the reviewer

Vitor Bianchi Lanzetta has a masters degree in Applied Economics from the University of So Paulo, one of the most reputable universities in Latin America. He has done a lot of research in economics using neural networks. He has also authored R Data Visualization Recipes, Packt Publishing . Vitor is very passionate about data science in general, and he walks the earth with a personal belief that he is just as cool as he is geek. He thinks that you will learn a lot from this book, and that TensorFlow may be the greatest deep learning tool currently available.

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