Amita Kapoor - Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition
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- Book:Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition
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Build cutting edge machine and deep learning systems for the lab, production, and mobile devices
Key Features- Understand the fundamentals of deep learning and machine learning through clear explanations and extensive code samples
- Implement graph neural networks, transformers using Hugging Face and TensorFlow Hub, and joint and contrastive learning
- Learn cutting-edge machine and deep learning techniques
Deep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using TensorFlow (TF) and Keras. Youll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available.
TensorFlow 2.x focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs based on Keras, and flexible model building on any platform. This book uses the latest TF 2.0 features and libraries to present an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical examples for the cloud, mobile, and large production environments.
This book also shows you how to create neural networks with TensorFlow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), recurrent neural networks (RNNs), natural language processing (NLP), and graph neural networks (GNNs)), covers working example apps, and then dives into TF in production, TF mobile, and TensorFlow with AutoML.
What you will learn- Learn how to use the popular GNNs with TensorFlow to carry out graph mining tasks
- Discover the world of transformers, from pretraining to fine-tuning to evaluating them
- Apply self-supervised learning to natural language processing, computer vision, and audio signal processing
- Combine probabilistic and deep learning models using TensorFlow Probability
- Train your models on the cloud and put TF to work in real environments
- Build machine learning and deep learning systems with TensorFlow 2.x and the Keras API
This hands-on machine learning book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice required to use Keras, TensorFlow, and AutoML to build machine learning systems.
Some machine learning knowledge would be useful. We dont assume TF knowledge.
Table of Contents- Neural Networks Foundations with TF
- Regression and Classification
- Convolutional Neural Networks
- Word Embeddings
- Recurrent Neural Network
- Transformers
- Unsupervised Learning
- Autoencoders
- Generative Models
- Self-Supervised Learning
- Reinforcement Learning
- Probabilistic TensorFlow
- An Introduction to AutoML
- The Math Behind Deep Learning
- Tensor Processing Unit
- Other Useful Deep Learning Libraries
- Graph Neural Networks
- Machine Learning Best Practices
- TensorFlow 2 Ecosystem
- Advanced Convolutional Neural Networks
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Deep Learning with TensorFlow and Keras
Third Edition
Build and deploy supervised, unsupervised, deep, and reinforcement learning models
Amita Kapoor
Antonio Gulli
Sujit Pal
BIRMINGHAMMUMBAI
Deep Learning with TensorFlow and Keras
Third Edition
Copyright 2022 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.
Lead Senior Publishing Product Manager: Tushar Gupta
Acquisition Editor Peer Reviews: Gaurav Gavas
Project Editor: Namrata Katare
Content Development Editor: Bhavesh Amin
Copy Editor: Safis Editing
Technical Editor: Aniket Shetty
Proofreader: Safis Editing
Indexer: Rekha Nair
Presentation Designer: Ganesh Bhadwalkar
First published: April 2017
Second edition: December 2019
Third edition: October 2022
Production reference: 1300922
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham
B3 2PB, UK.
ISBN 978-1-80323-291-1
www.packt.com
Approachable, well-written, with a great balance between theory and practice. A very enjoyable introduction to machine learning for software developers.
Franois Chollet,
Creator of Keras
Amita Kapoor taught and supervised research in the field of neural networks and artificial intelligence for 20+ years as an Associate Professor at the University of Delhi. At present, she works as an independent AI consultant and provides her expertise to various organizations working in the field of AI and EdTech.
First and foremost, I am thankful to the readers of this book. It is your encouragement via messages and emails that motivate me to give my best. I am extremely thankful to my co-authors, Antonio Gulli and Sujit Pal, for sharing their vast experience with me in writing this book. I am thankful to the entire Packt team for the effort they put in since the inception of this book and the reviewers who painstakingly went through the content and verified the code; their comments and suggestions helped improve the book.
Last but not the least, I am thankful to my teachers for their faith in me, my colleagues at the University of Delhi for their love and support, my friends for continuously motivating me, and my family members for their patience and love.
A part of the royalties of the book are donated.
Antonio Gulli has a passion for establishing and managing global technological talent for innovation and execution. His core expertise is in cloud computing, deep learning, and search engines. Currently, Antonio works for Google in the Cloud Office of the CTO in Zurich, working on Search, Cloud Infra, Sovereignty, and Conversational AI. Previously, he served as a founding member of the Office of the CTO in the EMEA. Earlier on, he served as Google Warsaw Site Director Leader, growing the site to 450+ engineers fully focused on cloud managing teams in GCE, Kubernetes, Serverless, Borg, and Console.
So far, Antonio has been lucky enough to gain professional experience in five countries in Europe and to manage teams in six countries in EMEA and the U.S:
- In Amsterdam, as Vice President for Elsevier, a leading scientific publisher.
- In London, as Principal Engineer for Bing Search, Microsoft.
- In Italy and the U.K, as CTO, Europe for Ask.com.
- In Poland, the U.K, and Switzerland with Google.
Antonio has co-invented a number of technologies for search, smart energy, and AI with 11 patents issued (21 applied) and published several books on coding and machine learning also translated into Japanese and Chinese. He speaks Spanish, English, and Italian and is currently learning Polish and French. Antonio is a proud father of Two boys, Lorenzo, 21 and Leonardo, 16, and a little queen, Aurora, 11.
I want to thank my sons, Lorenzo and Leonardo, and my daughter, Aurora, for being the motivation behind my perseverance. Also, I want to thank my partner, Nina, for being the North Star of my life in recent years.
Sujit Pal is a Technology Research Director at Elsevier Labs, an advanced technology group within the Reed-Elsevier Group of companies. His interests include semantic search, natural language processing, machine learning, and deep learning. At Elsevier, he has worked on several initiatives involving search quality measurement and improvement, image classification and duplicate detection, and annotation and ontology development for medical and scientific corpora.
Raghav Bali is a seasoned data science professional with over a decades experience in the research and development of large-scale solutions in finance, digital experience, IT infrastructure, and healthcare for giants such as Intel, American Express, UnitedHealth Group, and Delivery Hero. He is an innovator with 7+ patents, a published author of multiple well-received books (including Hands-On Transfer Learning with Python), has peer reviewed papers, and is a regular speaker in leading conferences on topics in the areas of machine learning, deep learning, computer vision, NLP, generative models, and augmented reality.
I would like to take this opportunity to congratulate the authors on yet another amazing book. Thanks to Packt for bringing me on board as a reviewer for this book, particularly Namrata, Saby, and Tushar for all their support and assistance and for being so receptive throughout the review process. And finally, Id like to thank my wife, family, and colleagues for all the support and patience.
Join our Discord community to meet like-minded people and learn alongside more than 2000 members at: https://packt.link/keras
Deep Learning with TensorFlow and Keras, Third Edition, is a concise yet thorough introduction to modern neural networks, artificial intelligence, and deep learning technologies designed especially for software engineers and data scientists. The book is the natural follow-up of the books Deep Learning with Keras [1] and TensorFlow 1.x Deep Learning Cookbook [2] previously written by the same authors.
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