Vasilev - Advanced deep learning with Python: design and implement adavnced next-generation AI solutions using TensorFlow and PyTorch

Copyright 2019 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(s), 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: Pravin Dhandre
Acquisition Editor: Devika Battike
Content Development Editor: Nathanya Dias
Senior Editor: Ayaan Hoda
Technical Editor: Manikandan Kurup
Copy Editor: Safis Editing
Project Coordinator: Aishwarya Mohan
Proofreader: Safis Editing
Indexer: Tejal Daruwale Soni
Production Designer: Nilesh Mohite

First published: December 2019

Production reference: 1111219

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

ISBN 978-1-78995-617-7

www.packt.com

Packt.com

Subscribe to our online digital library for full access to over 7,000 books and videos, as well as industry leading tools to help you plan your personal development and advance your career. For more information, please visit our website.

• Spend less time learning and more time coding with practical eBooks and Videos from over 4,000 industry professionals

• Improve your learning with Skill Plans built especially for you

• Get a free eBook or video every month

• Fully searchable for easy access to vital information

• Copy and paste, print, and bookmark content

Did you know that Packt offers eBook versions of every book published, with PDF and ePub files available? You can upgrade to the eBook version at www.packt.com and as a print book customer, you are entitled to a discount on the eBook copy. Get in touch with us at customercare@packtpub.com for more details.

At www.packt.com , you can also read a collection of free technical articles, sign up for a range of free newsletters, and receive exclusive discounts and offers on Packt books and eBooks.

Ivan Vasilev started working on the first open source Java deep learning library with GPU support in 2013. The library was acquired by a German company, where he continued to develop it. He has also worked as a machine learning engineer and researcher in the area of medical image classification and segmentation with deep neural networks. Since 2017, he has been focusing on financial machine learning. He is working on a Python-based platform that provides the infrastructure to rapidly experiment with different machine learning algorithms for algorithmic trading. Ivan holds an MSc degree in artificial intelligence from the University of Sofia, St. Kliment Ohridski.

Saibal Dutta has been working as an analytical consultant in SAS Research and Development. He is also pursuing a PhD in data mining and machine learning from IIT, Kharagpur. He holds an M.Tech in electronics and communication from the National Institute of Technology, Rourkela. He has worked at TATA communications, Pune, and HCL Technologies Limited, Noida, as a consultant. In his 7 years of consulting experience, he has been associated with global players including IKEA (in Sweden) and Pearson (in the US). His passion for entrepreneurship led him to create his own start-up in the field of data analytics. His areas of expertise include data mining, artificial intelligence, machine learning, image processing, and business consultation.

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.

This book is a collection of newly evolved deep learning models, methodologies, and implementations based on the areas of their application. In the first section of the book, you will learn about the building blocks of deep learning and the math behind neural networks (NNs). In the second section, you'll focus on convolutional neural networks (CNNs) and their advanced applications in computer vision (CV). You'll learn to apply the most popular CNN architectures in object detection and image segmentation. Finally, you'll discuss variational autoencoders and generative adversarial networks.
In the third section, you'll focus on natural language and sequence processing. You'll use NNs to extract sophisticated vector representations of words. You'll discuss various types of recurrent networks, such as long short-term memory (LSTM) and gated recurrent unit (GRU). Finally, you'll cover the attention mechanism to process sequential data without the help of recurrent networks. In the final section, you'll learn how to use graph NNs to process structured data. You'll cover meta-learning, which allows you to train an NN with fewer training samples. And finally, you'll learn how to apply deep learning in autonomous vehicles.
By the end of this book, you'll have gained mastery of the key concepts associated with deep learning and evolutionary approaches to monitoring and managing deep learning models.

This book is for data scientists, deep learning engineers and researchers, and AI developers who want to master deep learning and want to build innovative and unique deep learning projects of their own. This book will also appeal to those who are looking to get well-versed with advanced use cases and the methodologies adopted in the deep learning domain using real-world examples. Basic conceptual understanding of deep learning and a working knowledge of Python is assumed.