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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

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781803245409
Length 208 pages
Edition 1st Edition
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Author (1):
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Abdulrahman Kerim Abdulrahman Kerim
Author Profile Icon Abdulrahman Kerim
Abdulrahman Kerim
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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

The need for diverse data in ML

As we have discussed and seen in previous chapters, diverse training data improves the generalizability of ML models to new domains and contexts. In fact, diversity helps your ML-based solution to be more accurate and better applicable to real-world scenarios. Additionally, it makes it more robust to noise and anomalies, which are usually unavoidable in practice. For more information, please refer to Diversity in Machine Learning (https://arxiv.org/abs/1807.01477) and Performance of Machine Learning Algorithms and Diversity in Data (https://doi.org/10.1051/MATECCONF%2F201821004019).

Next, let’s highlight some of the main advantages of using diverse training data in ML. In general, training and validating your ML model on diverse datasets improve the following:

  • Transferability
  • Problem modeling
  • Security
  • The process of debugging
  • Robustness to anomalies
  • Creativity
  • Customer satisfaction

Now, let’s delve...

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