| Description |
1 online resource |
| 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 |
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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 -- Summary -- Chapter 3: Privacy Issues in Real Data -- Why is privacy an issue in ML? -- ML task -- Dataset size -- Regulations |
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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 -- Summary -- Part 2: An Overview of Synthetic Data for Machine Learning -- Chapter 4: An Introduction to Synthetic Data -- Technical requirements -- What is synthetic data? -- Synthetic and real data -- Data-centric and architecture-centric approaches in ML |
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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 -- Summary -- Chapter 5: Synthetic Data as a Solution -- The main advantages of synthetic data -- Unbiased -- Diverse -- Controllable -- Scalable -- Automatic data labeling -- Annotation quality -- Low cost |
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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 -- 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 |
| Subject |
Machine learning.
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Computer vision.
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Apprentissage automatique. |
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Vision par ordinateur. |
| Genre |
Electronic books.
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| Other Form: |
Print version: 1803245409 9781803245409 (OCoLC)1372132785 |
| ISBN |
9781803232607 (electronic bk.) |
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1803232609 (electronic bk.) |
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