Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Synthetic Data for Machine Learning

You're reading from   Synthetic Data for Machine Learning Revolutionize your approach to machine learning with this comprehensive conceptual guide

Arrow left icon
Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781803245409
Length 208 pages
Edition 1st Edition
Arrow right icon
Author (1):
Arrow left icon
Abdulrahman Kerim Abdulrahman Kerim
Author Profile Icon Abdulrahman Kerim
Abdulrahman Kerim
Arrow right icon
View More author details
Toc

Table of Contents (25) Chapters Close

Preface 1. Part 1:Real Data Issues, Limitations, and Challenges
2. Chapter 1: Machine Learning and the Need for Data FREE CHAPTER 3. Chapter 2: Annotating Real Data 4. Chapter 3: Privacy Issues in Real Data 5. Part 2:An Overview of Synthetic Data for Machine Learning
6. Chapter 4: An Introduction to Synthetic Data 7. Chapter 5: Synthetic Data as a Solution 8. Part 3:Synthetic Data Generation Approaches
9. Chapter 6: Leveraging Simulators and Rendering Engines to Generate Synthetic Data 10. Chapter 7: Exploring Generative Adversarial Networks 11. Chapter 8: Video Games as a Source of Synthetic Data 12. Chapter 9: Exploring Diffusion Models for Synthetic Data 13. Part 4:Case Studies and Best Practices
14. Chapter 10: Case Study 1 – Computer Vision 15. Chapter 11: Case Study 2 – Natural Language Processing 16. Chapter 12: Case Study 3 – Predictive Analytics 17. Chapter 13: Best Practices for Applying Synthetic Data 18. Part 5:Current Challenges and Future Perspectives
19. Chapter 14: Synthetic-to-Real Domain Adaptation 20. Chapter 15: Diversity Issues in Synthetic Data 21. Chapter 16: Photorealism in Computer Vision 22. Chapter 17: Conclusion 23. Index 24. Other Books You May Enjoy

Training a GAN

In this section, we will learn how to train a typical GAN. Then, we will discuss the main challenges and difficulties.

A GAN is trained using unsupervised learning techniques where both submodels are trained simultaneously using a process called adversarial training. A typical GAN consists of two neural networks (usually convolutional neural networks): the generator and the discriminator. The generator takes in a random noise vector as input and generates a synthetic (fake) sample. The goal of the generator is to produce synthetic data that is realistic and indistinguishable from real data. The discriminator, on the other hand, is trained to distinguish between real and fake samples. It receives a sample and predicts its data source domain: real or fake. If the discriminator correctly identifies a real data sample, no error is backpropagated. However, if the discriminator fails to identify a synthetic sample, it is penalized, and the generator is rewarded. The generator...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime