Synthetic Data for Machine Learning (eBook)
208 Seiten
Packt Publishing (Verlag)
978-1-80323-260-7 (ISBN)
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.
Conquer data hurdles, supercharge your ML journey, and become a leader in your field with synthetic data generation techniques, best practices, and case studiesKey FeaturesAvoid common data issues by identifying and solving them using synthetic data-based solutionsMaster synthetic data generation approaches to prepare for the future of machine learningEnhance performance, reduce budget, and stand out from competitors using synthetic dataPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe 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 learnUnderstand real data problems, limitations, drawbacks, and pitfallsHarness the potential of synthetic data for data-hungry ML modelsDiscover state-of-the-art synthetic data generation approaches and solutionsUncover synthetic data potential by working on diverse case studiesUnderstand synthetic data challenges and emerging research topicsApply synthetic data to your ML projects successfullyWho this book is forIf 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.]]>
| Erscheint lt. Verlag | 27.10.2023 |
|---|---|
| Sprache | englisch |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| ISBN-10 | 1-80323-260-9 / 1803232609 |
| ISBN-13 | 978-1-80323-260-7 / 9781803232607 |
| Haben Sie eine Frage zum Produkt? |
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