Book Image

Synthetic Data for Machine Learning

By : Abdulrahman Kerim
Book Image

Synthetic Data for Machine Learning

By: Abdulrahman Kerim

Overview of this book

The machine learning (ML) revolution has made our world unimaginable without its products and services. However, training ML models requires vast datasets, which entails a process plagued by high costs, errors, and privacy concerns associated with collecting and annotating real data. Synthetic data emerges as a promising solution to all these challenges. This book is designed to bridge theory and practice of using synthetic data, offering invaluable support for your ML journey. Synthetic Data for Machine Learning empowers you to tackle real data issues, enhance your ML models' performance, and gain a deep understanding of synthetic data generation. You’ll explore the strengths and weaknesses of various approaches, gaining practical knowledge with hands-on examples of modern methods, including Generative Adversarial Networks (GANs) and diffusion models. Additionally, you’ll uncover the secrets and best practices to harness the full potential of synthetic data. By the end of this book, you’ll have mastered synthetic data and positioned yourself as a market leader, ready for more advanced, cost-effective, and higher-quality data sources, setting you ahead of your peers in the next generation of ML.

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Synthetic Data for Machine Learning
Abdulrahman Kerim
Oct, 2023 | 208 pages
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Table of Contents (25 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Conventions used
Get in touch
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1
Part 1:Real Data Issues, Limitations, and Challenges
Part 1:Real Data Issues, Limitations, and Challenges
Free Chapter
2
Chapter 1: Machine Learning and the Need for Data
Chapter 1: Machine Learning and the Need for Data
Technical requirements
Artificial intelligence, machine learning, and deep learning
Why are ML and DL so powerful?
Training ML models
Summary
3
Chapter 2: Annotating Real Data
Chapter 2: Annotating Real Data
Annotating data for ML
Issues with the annotation process
Optical flow and depth estimation
Summary
4
Chapter 3: Privacy Issues in Real Data
Chapter 3: Privacy Issues in Real Data
Why is privacy an issue in ML?
What exactly is the privacy problem in ML?
Privacy-preserving ML
Real data challenges and issues
Summary
5
Part 2:An Overview of Synthetic Data for Machine Learning
Part 2:An Overview of Synthetic Data for Machine Learning
6
Chapter 4: An Introduction to Synthetic Data
Chapter 4: An Introduction to Synthetic Data
Technical requirements
What is synthetic data?
History of synthetic data
Synthetic data types
Data augmentation
Summary
7
Chapter 5: Synthetic Data as a Solution
Chapter 5: Synthetic Data as a Solution
The main advantages of synthetic data
Solving privacy issues with synthetic data
Using synthetic data to solve time and efficiency issues
Synthetic data as a revolutionary solution for rare data
Synthetic data generation methods
Summary
8
Part 3:Synthetic Data Generation Approaches
Part 3:Synthetic Data Generation Approaches
9
Chapter 6: Leveraging Simulators and Rendering Engines to Generate Synthetic Data
Chapter 6: Leveraging Simulators and Rendering Engines to Generate Synthetic Data
Introduction to simulators and rendering engines
Generating synthetic data
Challenges and limitations
Looking at two case studies
Summary
10
Chapter 7: Exploring Generative Adversarial Networks
Chapter 7: Exploring Generative Adversarial Networks
Technical requirements
What is a GAN?
Training a GAN
Utilizing GANs to generate synthetic data
Hands-on GANs in practice
Variations of GANs
Summary
11
Chapter 8: Video Games as a Source of Synthetic Data
Chapter 8: Video Games as a Source of Synthetic Data
The impact of the video game industry
Generating synthetic data using video games
Challenges and limitations
Summary
12
Chapter 9: Exploring Diffusion Models for Synthetic Data
Chapter 9: Exploring Diffusion Models for Synthetic Data
Technical requirements
An introduction to diffusion models
Diffusion models – the pros and cons
Hands-on diffusion models in practice
Diffusion models – ethical issues
Summary
13
Part 4:Case Studies and Best Practices
Part 4:Case Studies and Best Practices
14
Chapter 10: Case Study 1 – Computer Vision
Chapter 10: Case Study 1 – Computer Vision
Transforming industries – the power of computer vision
Synthetic data and computer vision – examples from industry
Summary
15
Chapter 11: Case Study 2 – Natural Language Processing
Chapter 11: Case Study 2 – Natural Language Processing
A brief introduction to NLP
The need for large-scale training datasets in NLP
Hands-on practical example with ChatGPT
Synthetic data as a solution for NLP problems
Summary
16
Chapter 12: Case Study 3 – Predictive Analytics
Chapter 12: Case Study 3 – Predictive Analytics
What is predictive analytics?
Predictive analytics issues with real data
Case studies of utilizing synthetic data for predictive analytics
Summary
17
Chapter 13: Best Practices for Applying Synthetic Data
Chapter 13: Best Practices for Applying Synthetic Data
Unveiling the challenges of generating and utilizing synthetic data
Domain-specific issues limiting the usability of 
synthetic data
Best practices for the effective utilization of synthetic data
Summary
18
Part 5:Current Challenges and Future Perspectives
Part 5:Current Challenges and Future Perspectives
19
Chapter 14: Synthetic-to-Real Domain Adaptation
Chapter 14: Synthetic-to-Real Domain Adaptation
The domain gap problem in ML
Approaches for synthetic-to-real domain adaptation
Synthetic-to-real domain adaptation – issues and challenges
Summary
20
Chapter 15: Diversity Issues in Synthetic Data
Chapter 15: Diversity Issues in Synthetic Data
The need for diverse data in ML
Generating diverse synthetic datasets
Diversity issues in the synthetic data realm
Summary
21
Chapter 16: Photorealism in Computer Vision
Chapter 16: Photorealism in Computer Vision
Synthetic data photorealism for computer vision
Photorealism approaches
Photorealism evaluation metrics
Challenges and limitations of photorealistic synthetic data
Summary
22
Chapter 17: Conclusion
Chapter 17: Conclusion
Real data and its problems
Synthetic data as a solution
Real-world case studies
Challenges and limitations
Future perspectives
Summary
23
Index
Index
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24
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Preface

Machine learning (ML) has made our lives far easier. We cannot imagine our world without ML-based products and services. ML models need to be trained on large-scale datasets to perform well. However, collecting and annotating real data is extremely expensive, error-prone, and subject to privacy issues, to name a few disadvantages. Synthetic data is a promising solution to real-data ML-based solutions.

Synthetic Data for Machine Learning is a unique book that will help you master synthetic data, designed to make your learning journey enjoyable. In this book, theory and good practice complement each other to provide leading-edge support!

The book helps you to overcome real data issues and improve your ML models’ performance. It provides an overview of the fundamentals of synthetic data generation and discusses the pros and cons of each approach. It reveals the secrets of synthetic data and the best practices to leverage it better.

By the end of this book, you will have mastered synthetic data and increased your chances of becoming a market leader. It will enable you to springboard into a more advanced, cheaper, and higher-quality data source, making you well prepared and ahead of your peers for the next generation of ML!

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