Generative AI in R

Leverage Generative AI within the R programming environment and prepare for future directions and how new innovations can be applied in the R ecosystem. This pioneering book is designed to bridge the gap between the advanced realms of Generative AI and the practical, statistical computing power of R.

You ll begin with an introduction to Generative AI principles and its significance in the current data-driven landscape. You ll then dive into the practicalities of implementing generative models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) in R. See how R, most known for its statistical analysis, can also be used for creative synthetic data, improving model robustness, and generating innovative insights from data.

Additionally, this book addresses the demand for ethical AI by emphasizing the use of synthetic data to tackle privacy and data scarcity issues concerns particularly relevant in healthcare, finance, and social research. We are at a pivotal moment in the evolution of AI and data science. With AI's growing importance, the book's focus on R makes advanced techniques more accessible, promoting ethical and innovative data science practice, preparing readers for upcoming trends.

What You Will Learn

• Grasp the core concepts of Generative AI and its significance in the broader AI landscape.
• Implement various generative models in R, such as GANS and VAEs.
• Generate high-quality synthetic data.

1. Introduction to Generative AI and R. - 2. Setting up your R Environment for Generative AI. - 3. Fundamentals of Generative AI . - 4. Implementing Basic Generative Models in R. - 5. Generating Synthetic Data with R. - 6. Advanced Generative Models and Techniques. - 7. Generative AI for Predictive Modeling. - 8. Creative Applications of Generative AI in R. - 9. Ethical Considerations and Future Directions. - 10. Capstone Projects and Future Roadmap with R for Generative AI.