Synthetic Data for Machine Learning : Revolutionize Your Approach to Machine Learning with This Comprehensive Conceptual Guide.

Format:

Book

Author/Creator:

Kerim, Abdulrahman.

Language:

English

Subjects (All):

Machine learning.

Computer vision.

Physical Description:

1 online resource (209 pages)

Edition:

1st ed.

Place of Publication:

Birmingham : Packt Publishing, Limited, 2023.

Summary:

Conquer data hurdles, supercharge your ML journey, and become a leader in your field with synthetic data generation techniques, best practices, and case studies Key Features Avoid common data issues by identifying and solving them using synthetic data-based solutions Master synthetic data generation approaches to prepare for the future of machine learning Enhance performance, reduce budget, and stand out from competitors using synthetic data Purchase of the print or Kindle book includes a free PDF eBook Book Description 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. What you will learn Understand real data problems, limitations, drawbacks, and pitfalls Harness the potential of synthetic data for data-hungry ML models Discover state-of-the-art synthetic data generation approaches and solutions Uncover synthetic data potential by working on diverse case studies Understand synthetic data challenges and emerging research topics Apply synthetic data to your ML projects successfully Who this book is for If you are a machine learning (ML) practitioner or researcher who wants to overcome data problems, this book is for you. Basic knowledge of ML and Python programming is required. The book is one of the pioneer works on the subject, providing leading-edge support for ML engineers, researchers, companies, and decision makers.

Contents:

Cover

Title Page

Copyright and Credits

Dedications

Contributors

Table of Contents

Part 1: Real Data Issues, Limitations, and Challenges

Chapter 1: Machine Learning and the Need for Data

Technical requirements

Artificial intelligence, machine learning, and deep learning

Artificial intelligence (AI)

Machine learning (ML)

Deep learning (DL)

Why are ML and DL so powerful?

Feature engineering

Transfer across tasks

Training ML models

Collecting and annotating data

Designing and training an ML model

Validating and testing an ML model

Iterations in the ML development process

Summary

Chapter 2: Annotating Real Data

Annotating data for ML

Learning from data

Training your ML model

Testing your ML model

Issues with the annotation process

The annotation process is expensive

The annotation process is error-prone

The annotation process is biased

Optical flow and depth estimation

Ground truth generation for computer vision

Optical flow estimation

Depth estimation

Chapter 3: Privacy Issues in Real Data

Why is privacy an issue in ML?

ML task

Dataset size

Regulations

What exactly is the privacy problem in ML?

Copyright and intellectual property infringement

Privacy and reproducibility of experiments

Privacy issues and bias

Privacy-preserving ML

Approaches for privacy-preserving datasets

Approaches for privacy-preserving ML

Real data challenges and issues

Part 2: An Overview of Synthetic Data for Machine Learning

Chapter 4: An Introduction to Synthetic Data

What is synthetic data?

Synthetic and real data

Data-centric and architecture-centric approaches in ML

History of synthetic data

Random number generators

Generative Adversarial Networks (GANs).

Synthetic data for privacy issues

Synthetic data in computer vision

Synthetic data and ethical considerations

Synthetic data types

Data augmentation

Geometric transformations

Noise injection

Text replacement, deletion, and injection

Chapter 5: Synthetic Data as a Solution

The main advantages of synthetic data

Unbiased

Diverse

Controllable

Scalable

Automatic data labeling

Annotation quality

Low cost

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

Part 3: Synthetic Data Generation Approaches

Chapter 6: Leveraging Simulators and Rendering Engines to Generate Synthetic Data

Introduction to simulators and rendering engines

Simulators

Rendering and game engines

History and evolution of simulators and game engines

Generating synthetic data

Identify the task and ground truth to generate

Create the 3D virtual world in the game engine

Setting up the virtual camera

Adding noise and anomalies

Setting up the labeling pipeline

Generating the training data with the ground truth

Challenges and limitations

Realism

Diversity

Complexity

Looking at two case studies

AirSim

CARLA

Chapter 7: Exploring Generative Adversarial Networks

What is a GAN?

Training a GAN

GAN training algorithm

Training loss

Challenges

Utilizing GANs to generate synthetic data

Hands-on GANs in practice

Variations of GANs

Conditional GAN (cGAN)

CycleGAN

Conditional Tabular GAN (CTGAN)

Wasserstein GAN (WGAN) and Wasserstein GAN with Gradient Penalty (WGAN-GP)

f-GAN

DragGAN

Chapter 8: Video Games as a Source of Synthetic Data.

The impact of the video game industry

Photorealism and the real-synthetic domain shift

Time, effort, and cost

Generating synthetic data using video games

Utilizing games for general data collection

Utilizing games for social studies

Utilizing simulation games for data generation

Controllability

Game genres and limitations on synthetic data generation

Ethical issues

Intellectual property

Chapter 9: Exploring Diffusion Models for Synthetic Data

An introduction to diffusion models

The training process of DMs

Applications of DMs

Diffusion models - the pros and cons

The pros of using DMs

The cons of using DMS

Hands-on diffusion models in practice

Context

Dataset

ML model

Training

Testing

Diffusion models - ethical issues

Copyright

Bias

Inappropriate content

Responsibility

Privacy

Fraud and identity theft

Part 4: Case Studies and Best Practices

Chapter 10: Case Study 1 - Computer Vision

Transforming industries - the power of computer vision

The four waves of the industrial revolution

Industry 4.0 and computer vision

Synthetic data and computer vision - examples from industry

Neurolabs using synthetic data in retail

Microsoft using synthetic data alone for face analysis

Synthesis AI using synthetic data for virtual try-on

Chapter 11: Case Study 2 - Natural Language Processing

A brief introduction to NLP

Applications of NLP in practice

The need for large-scale training datasets in NLP

Human language complexity

Contextual dependence

Generalization

Hands-on practical example with ChatGPT

Synthetic data as a solution for NLP problems

SYSTRAN Soft's use of synthetic data

Telefónica's use of synthetic data.

Clinical text mining utilizing synthetic data

The Alexa virtual assistant model

Chapter 12: Case Study 3 - Predictive Analytics

What is predictive analytics?

Applications of predictive analytics

Predictive analytics issues with real data

Partial and scarce training data

Cost

Case studies of utilizing synthetic data for predictive analytics

Provinzial and synthetic data

Healthcare benefits from synthetic data in predictive analytics

Amazon fraud transaction prediction using synthetic data

Chapter 13: Best Practices for Applying Synthetic Data

Unveiling the challenges of generating and utilizing synthetic data

Domain gap

Data representation

Privacy, security, and validation

Trust and credibility

Domain-specific issues limiting the usability of synthetic data

Healthcare

Finance

Autonomous cars

Best practices for the effective utilization of synthetic data

Part 5: Current Challenges and Future Perspectives

Chapter 14: Synthetic-to-Real Domain Adaptation

The domain gap problem in ML

Sensitivity to sensors' variations

Discrepancy in class and feature distributions

Concept drift

Approaches for synthetic-to-real domain adaptation

Domain randomization

Adversarial domain adaptation

Feature-based domain adaptation

Synthetic-to-real domain adaptation - issues and challenges

Unseen domain

Limited real data

Computational complexity

Synthetic data limitations

Multimodal data complexity

Chapter 15: Diversity Issues in Synthetic Data

The need for diverse data in ML

Transferability

Better problem modeling

Security

Process of debugging

Robustness to anomalies

Creativity

Inclusivity

Generating diverse synthetic datasets

Latent space variations.

Ensemble synthetic data generation

Diversity regularization

Incorporating external knowledge

Progressive training

Procedural content generation with game engines

Diversity issues in the synthetic data realm

Balancing diversity and realism

Privacy and confidentiality concerns

Validation and evaluation challenges

Chapter 16: Photorealism in Computer Vision

Synthetic data photorealism for computer vision

Feature extraction

Robustness

Benchmarking performance

Photorealism approaches

Physically Based Rendering (PBR)

Neural style transfer

Photorealism evaluation metrics

Structural Similarity Index Measure (SSIM)

Learned Perceptual Image Patch Similarity (LPIPS)

Expert evaluation

Challenges and limitations of photorealistic synthetic data

Creating hyper-realistic scenes

Resources versus photorealism trade-off

Chapter 17: Conclusion

Real data and its problems

Synthetic data as a solution

Real-world case studies

Future perspectives

Index

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Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9781803232607

1803232609

OCLC:

1406406795