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Key benefits

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

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.

Who is this book 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.

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

What do you get with a Packt Subscription?

Free for first 7 days. £13.99 p/m after that. Cancel any time!

Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!

50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

Thousands of reference materials covering every tech concept you need to stay up to date.

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Pratik Bhavsar Dec 22, 2023

There is a hot debate in the market ❗If humans are trained with human generated data.There is nothing wrong about training AI with AI generated data!Discover the future of Machine Learning with the latest book "𝗦𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗗𝗮𝘁𝗮 𝗳𝗼𝗿 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴" by synthetic data researcher Abdulrahman Kerim!About the bookIn a world where machine learning has become a game-changer, the struggle to acquire large datasets is real – costly, error-prone, and laden with privacy concerns. The book addresses these challenges head-on, offering practical insights into the world of synthetic data generation.What sets it apart- All types of data: Explains approaches for text, image, numerical, RLHF data.- Cutting-edge techniques: Master the art of synthetic data generation to future-proof your machine learning endeavors.- Real-world Solutions: Learn to tackle genuine data issues using synthetic data-based solutions.- Practical benefits: Enhance model performance, cut costs, and gain a competitive edge in your field.What you'll explore- Data Realities: Understand the nuances, limitations, and pitfalls associated with real data.- In-depth Solutions: Discover state-of-the-art synthetic data generation approaches through hands-on case studies.

Amazon Verified review

Dror Nov 29, 2023

Deep learning (DL) has taken the world of AI and machine learning (ML) by storm. While research on more efficient approaches for AI does exist in academia and large research labs, the leading paradigm for successful real-world applications of DL remains supervised learning, or learning from labeled data. Deep neural networks trained using supervised learning are extremely powerful, but typically require very large amounts of labeled data, which is costly and time-consuming to acquire.This unique book provides a broad and detailed coverage of using synthetic data for training ML and DL systems. Generation and usage of synthetic data are believed to play an increasingly important role in training and building large neural networks in the future, and this book is a unique and practical guide to understanding the usage and generation of synthetic data for building AI systems effectively and efficiently.The book begins with a concise introduction to the challenges of acquiring and using real data for building ML systems, followed by a clear introduction to using synthetic data for ML. The main section of the book deals with synthetic data generation methods, such as using rendering engines and simulators, generative adversarial networks (GANs), video games, and diffusion models (DDPMs) for synthetic data generation. The last part of the book provides helpful examples of related case studies, as well as best practices and potential challenges of using synthetic data in ML. The accompanying GitHub repo is also helpful in reinforcing the materials and concepts presented in the book.This book will benefit any data scientist, researcher, or machine learning practitioner who develops ML/DL models and wants to learn how to leverage synthetic data generation. Some understanding of machine learning and deep learning concepts, as well as basic familiarity with the Python programming language, are all you need to use and benefit from this practical guide.Highly recommended!

Amazon Verified review

Om S Nov 27, 2023

This book is a game-changer for machine learning practitioners and researchers. Focusing on synthetic data, it addresses common challenges in ML, offering innovative solutions and best practices. From understanding real data problems to mastering advanced synthetic data generation methods like GANs, the book equips readers to enhance model performance and overcome limitations. Diverse case studies, including computer vision and natural language processing, showcase practical applications. A must-read for ML engineers and decision-makers, this book positions you at the forefront of the ML revolution, ready to harness the power of synthetic data for superior outcomes.

Amazon Verified review

Advitya Gemawat Dec 08, 2023

I found the book to be quite an informative guide on generating, augmenting, and anonymizing data for various ML tasks.Here are some of the highlights of the book that I found interesting and fun to learn about:📈 The book covers a wide range of synthetic data techniques, such as data simulation, data augmentation, data anonymization, data synthesis, and data imputation. It also provides practical examples and code snippets for each technique using Python libraries like NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch. Part of these concepts were also a good revision for me from a Data Science Applications class I took in college :)😎 One of the more useful chapters for me felt to be Chapter 7, where the author explains how to use generative adversarial networks (GANs) to synthesize realistic images, text, and audio. The author included different types of GANs, such as cGAN, WGAN, CycleGAN, StyleGAN, TextGAN etc, and corresponding evaluation metrics such as inception score, FID, and BLEU.🔒 I liked the additional context the book included outside of just generating synthetic data. The book also touched upon the broader area of 'Privacy-Preserving ML' with topics such as Differential Privacy and Federated Learning to protect privacy of individuals / groups in datasets and measure the trade-off between utility & privacy.📊 The book also contains several case studies and projects that demonstrate how to apply synthetic data techniques to real-world problems, such as face detection, sentiment analysis, speech recognition, and fraud detection, and overcome challenges like data scarcity, data imbalance, and data leakage.I found this book was pretty densely packed with content which can benefit readers at all levels. As an ML practitioner who has dealt with synthetic data generation in the past, I was happy how the book structured the content into 5 broad themes, 17 chapters, and multiple hierarchies of subtopics, which made me navigate the content easier and spend more time on specific areas I was more intrigued about.

Amazon Verified review

tt0507 Nov 30, 2023

The book on synthetic data is praised for its extensive coverage of the topic, emphasizing its relevance in the AI and machine learning landscape. It starts with a solid introduction to the challenges of using real data, making it accessible for both beginners and experienced practitioners. The main sections explore various synthetic data generation methods, including GANs and simulation-based approaches like video games. The book provides theoretical explanations, practical examples, and case studies, offering a comprehensive guide for readers seeking to leverage synthetic data effectively. It is hailed as a game-changer for machine learning professionals, addressing common challenges and providing innovative solutions, making it a must-read for those wanting to stay at the forefront of the ML revolution.

Amazon Verified review

Synthetic Data for Machine Learning: Revolutionize your approach to machine learning with this comprehensive conceptual guide

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Synthetic Data for Machine Learning: Revolutionize your approach to machine learning with this comprehensive conceptual guide

What do you get with a Packt Subscription?

Key benefits

Description

Who is this book for?

What you will learn

Product Details

What do you get with a Packt Subscription?

Product Details

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