Joseph Babcock - Generative AI with Python and TensorFlow 2: Create images, text, and music with VAEs, GANs, LSTMs, Transformer models
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- Book:Generative AI with Python and TensorFlow 2: Create images, text, and music with VAEs, GANs, LSTMs, Transformer models
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Fun and exciting projects to learn what artificial minds can create
Key Features- Code examples are in TensorFlow 2, which make it easy for PyTorch users to follow along
- Look inside the most famous deep generative models, from GPT to MuseGAN
- Learn to build and adapt your own models in TensorFlow 2.x
- Explore exciting, cutting-edge use cases for deep generative AI
Machines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI?
In this book, youll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. Youll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks.
Theres been an explosion in potential use cases for generative models. Youll look at Open AIs news generator, deepfakes, and training deep learning agents to navigate a simulated environment.
Recreate the code thats under the hood and uncover surprising links between text, image, and music generation.
What you will learn- Export the code from GitHub into Google Colab to see how everything works for yourself
- Compose music using LSTM models, simple GANs, and MuseGAN
- Create deepfakes using facial landmarks, autoencoders, and pix2pix GAN
- Learn how attention and transformers have changed NLP
- Build several text generation pipelines based on LSTMs, BERT, and GPT-2
- Implement paired and unpaired style transfer with networks like StyleGAN
- Discover emerging applications of generative AI like folding proteins and creating videos from images
This is a book for Python programmers who are keen to create and have some fun using generative models. To make the most out of this book, you should have a basic familiarity with math and statistics for machine learning.
Table of Contents- An Introduction to Generative AI: Drawing Data from Models
- Setting Up a TensorFlow Lab
- Building Blocks of Deep Neural Networks
- Teaching Networks to Generate Digits
- Painting Pictures with Neural Networks Using VAEs
- Image Generation with GANs
- Style Transfer with GANs
- Deepfakes with GANs
- The Rise of Methods for Text Generation
- NLP 2.0: Using Transformers to Generate Text
- Composing Music with Generative Models
- Play Video Games with Generative AI: GAIL
- Emerging Applications in Generative AI
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Generative AI with Python and TensorFlow 2
Harness the power of generative models to create images, text, and music
Joseph Babcock
Raghav Bali
BIRMINGHAM - MUMBAI
Generative AI with Python and TensorFlow 2
Copyright 2021 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.
Producer: Tushar Gupta
Acquisition Editor Peer Reviews: Suresh Jain, Saby D'silva
Content Development Editors: Lucy Wan, Joanne Lovell
Technical Editor: Gaurav Gavas
Project Editor: Janice Gonsalves
Copy Editor: Safis Editing
Proofreader: Safis Editing
Indexer: Pratik Shirodkar
Presentation Designer: Pranit Padwal
First published: April 2021
Production reference: 1280421
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham B3 2PB, UK.
ISBN 978-1-80020-088-3
www.packt.com
Joseph Babcock has over a decade of experience in machine learning and developing big data solutions. He applied predictive modeling to drug discovery and genomics during his doctoral studies in neurosciences, and has since worked and led data science teams in the streaming media, e-commerce, and financial services industries. He previously authored Mastering Predictive Analytics with Python and Python: Advanced Predictive Analytics with Packt.
I would like to acknowledge my family for their support during the composition of this book.
Raghav Bali is a data scientist and a published author. He has led advanced analytics initiatives working with several Fortune 500 companies like Optum (UHG), Intel, and American Express. His work involves research and development of enterprise solutions leveraging machine learning and deep learning. He holds a Master of Technology degree (gold medalist) from IIIT Bangalore, with specializations in machine learning and software engineering. Raghav has authored several books on R, Python, machine learning, and deep learning, including Hands-On Transfer Learning with Python.
To my wife, parents, and brother, without whom this would not have been possible. To all the researchers whose work continues to inspire me to learn. And to my co-author, reviewers, and the Packt team (especially Tushar, Janice, and Lucy) for their hard work in transforming our work into this amazing book.
Hao-Wen Dong is currently a PhD student in Computer Science and Engineering at the University of California, San Diego, working with Prof. Julian McAuley and Prof. Taylor Berg-Kirkpatrick. His research interests lie at the intersection of music and machine learning, with a recent focus on music generation. He is interested in building tools that could lower the barrier of entry for music composition and potentially lead to the democratization of music creation. Previously, he did a research internship in the R&D Division at Yamaha Corporation. Before that, he was a research assistant in the Music and AI Lab directed by Dr. Yi-Hsuan Yang at Academia Sinica. He received his bachelor's degree in Electrical Engineering from National Taiwan University.
Gokula Krishnan Santhanam is a Python developer who lives in Zurich, Switzerland. He has been working with deep learning techniques for more than 5 years. He has worked on problems in generative modeling, adversarial attacks, interpretability, and predictive maintenance while working at IBM Research and interning at Google. He finished his master's in Computer Science at ETH Zurich and his bachelor's at BITS Pilani. When he's not working, you can find him enjoying board games with his wife or hiking in the beautiful Alps.
I would like to thank my wife, Sadhana, for her continuous help and support and for always being there when I need her.
In this book we will explore generative AI, a cutting-edge technology for generating synthetic (yet strikingly realistic) data using advanced machine learning algorithms. Generative models have been intriguing researchers across domains for quite some time now. With recent improvements in the fields of machine learning and more specifically deep learning, generative modeling has seen a tremendous uptick in the number of research works and their applications across different areas. From artwork and music composition to synthetic medical datasets, generative modeling is pushing the boundaries of imagination and intelligence alike. The amount of thought and effort required to understand, implement, and utilize such methods is simply amazing. Some of the newer methods (such as GANs) are very powerful, yet difficult to control, making the overall learning process both exciting and frustrating.
Generative AI with Python and TensorFlow 2 is the result of numerous hours of hard work by us authors and the talented team at Packt Publishing to help you understand this deep, wide, and wild space of generative modeling. The aim of this book is to be a kaleidoscope of the generative modeling space and cover a wide range of topics. This book takes you on a journey where you don't just read the theory and learn about the fundamentals, but you also discover the potential and impact of these models through worked examples. We will implement these models using a variety of open-source technologies the Python programming language, the TensorFlow 2 library for deep neural network development, and cloud computing resources such as Google Colab and the Kubeflow project.
Having an understanding of the various topics, models, architectures, and examples in this book will help you explore more complex topics and cutting-edge research with ease.
Generative AI with Python and TensorFlow 2 is for data scientists, ML engineers, researchers, and developers with an interest in generative modeling and the application of state-of-the-art architectures on real world datasets. This book is also suitable for TensorFlow beginners with intermediate-level deep learning-related skills who are looking to expand their knowledge base.
Basic proficiency in Python and deep learning is all that is required to get started with this book.
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