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Dipanjan Sarkar - Hands-On Transfer Learning with Python Implement Advanced Deep Learning and Neural Network Models Using TensorFlow and Keras

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Hands-On Transfer Learning with Python Implement Advanced Deep Learning and Neural Network Models Using TensorFlow and Keras: summary, description and annotation

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Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystemKey Features Build deep learning models with transfer learning principles in Python implement transfer learning to solve real-world research problems Perform complex operations such as image captioning neural style transferBook DescriptionTransfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems.The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples.The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP).By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems.What you will learn Set up your own DL environment with graphics processing unit (GPU) and Cloud support Delve into transfer learning principles with ML and DL models Explore various DL architectures, including CNN, LSTM, and capsule networks Learn about data and network representation and loss functions Get to grips with models and strategies in transfer learning Walk through potential challenges in building complex transfer learning models from scratch Explore real-world research problems related to computer vision and audio analysis Understand how transfer learning can be leveraged in NLPWho this book is forHands-On Transfer Learning with Python is for data scientists, machine learning engineers, analysts and developers with an interest in data and applying state-of-the-art transfer learning methodologies to solve tough real-world problems. Basic proficiency in machine learning and Python is required.

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Hands-On Transfer Learning with Python
Implement advanced deep learning and neural network models using TensorFlow and Keras
Dipanjan Sarkar
Raghav Bali
Tamoghna Ghosh

BIRMINGHAM - MUMBAI Hands-On Transfer Learning with Python Copyright 2018 - photo 2

BIRMINGHAM - MUMBAI
Hands-On Transfer Learning with Python

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 authors, 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: Sunith Shetty
Acquisition Editor: Tushar Gupta
Content Development Editor: Unnati Guha
Technical Editor: Sayli Nikalje
Copy Editor: Safis Editing
Project Coordinator: Manthan Patel
Proofreader: Safis Editing
Indexer: Rekha Nair
Graphics: Jisha Chirayil
Production Coordinator: Shantanu Zagade

First published: August 2018

Production reference: 1300818

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

ISBN 978-1-78883-130-7

www.packtpub.com

This book wouldn't have been possible without several people who made this from a mere concept into reality. I would like to thank my parents, Digbijoy and Sampa, my partner, Durba, my pets, family, and friends for supporting me constantly in my endeavors. A big thank you to the entire team at Packt especially Tushar, Sayli, and Unnati for working tirelessly and supporting us throughout our journey. Also thanks to Matthew Mayo for gracing our book with his foreword and doing great things with KDnuggets.

Thanks to Adrian Rosebrock and PyImageSearch for some excellent visuals and content around pretrained models for computer vision, Federico Baldassarre, Diego Gonzalez-Morin, Lucas Rodes-Guirao, and Emil Wallner for some excellent strategies and implementations for image colorization, Anurag Mishra for giving tips for build an efficient image captioning model, Franois Chollet for building Keras and writing some very useful and engaging content on transfer learning and to the entire Python AI eco-system for helping the community democratize deep learning and artificial intelligence for everyone.

Finally, I would like to thank my managers and mentors Gopalan, Sanjeev, and Nagendra and all my friends and colleagues at Intel for encouraging me and giving me the opportunity to explore new domains in the world of AI. Shoutout also to the folks from Springboard, especially Srdjan Santic for not just giving me an opportunity to learn and interact with some amazing people but also for the passion, zeal, and vision of educating more people on Data Science and AI. Towards Data Science and Ludovic Benistant thanks for helping me learn and share more about AI to the rest of the world and helping me explore cutting-edge research and work in these domains. Last but not the least, I owe a ton of gratitude to my co-authors Raghav and Tamoghna and our reviewer Nitin Panwar for embarking on this journey with me and without whom this book wouldn't have been possible!

Dipanjan Sarkar

I would like to take this opportunity to express gratitude to my parents, Sunil and Neeru, my wife, Swati, my brother, Rajan, family, teachers, friends, colleagues, and mentors who have encouraged, supported and taught me over the years. I would also like to thank my co-authors and good friends Dipanjan Sarkar and Tamoghna Ghosh, for taking me along on this amazing journey. A big thanks to my managers and mentors Vineet, Ravi, and Vamsi along with all my teammates at Optum for their support and encouragement to explore new domains in the Data Science world.

I would like to thank Tushar Gupta, Aaryaman Singh, Sayli Nikalje, Unnati Guha, and Packt for the opportunity and their support throughout this journey. This book wouldn't have been complete without Nitin Panwar's insightful feedback and suggestions. Last but not the least, special thanks to Franois Chollet for Keras, the Python ecosystem and community, fellow authors and researchers who are striving every day to bring these amazing technologies and tools at our fingertips.

Raghav Bali

I would like to thank entire Packt team for giving me this unique opportunity and also guiding me throughout the journey. For this book my co-authors here acted as my mentor as well. They helped me with their insightful suggestions and guidance. Thanks to Nitin for patiently reviewing this book and providing great feedback. I would like to thank my wife Doyel, my son Anurag, and my parents for being a constant source of inspiration for me and tolerating me for working extended hours. Also, I am grateful to my Intel managers for their encouragement and support.

Tamoghna Ghosh

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Foreword

Chances are you are familiar with the recent and seemingly endless machine learning innovations, but do you know about what goes into training a machine learning model? Generally, a given machine learning model is trained on specific data for a particular task. This training process can be exceptionally resource and time-consuming, and since the resulting models are task-specific, the maximum potential of the resulting model is not realized.

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