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Generative AI in Rtransforming data ...

Singh, Akansha.

Generative AI in Rtransforming data science with synthetic data and advanced modeling techniques /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Generative AI in Rby Akansha Singh, Krishna Kant Singh.
Reminder of title:	transforming data science with synthetic data and advanced modeling techniques /
Author:	Singh, Akansha.
other author:	Singh, Krishna Kant.
Published:	Berkeley, CA :Apress :2025.
Description:	xvi, 580 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence.
Online resource:	https://doi.org/10.1007/979-8-8688-1763-2
ISBN:	9798868817632$q(electronic bk.)

Generative AI in Rtransforming data science with synthetic data and advanced modeling techniques /
Singh, Akansha.

Generative AI in Rtransforming data science with synthetic data and advanced modeling techniques /[electronic resource] :by Akansha Singh, Krishna Kant Singh. - Berkeley, CA :Apress :2025. - xvi, 580 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9798868817632$q(electronic bk.)

Standard No.: 10.1007/979-8-8688-1763-2doiSubjects--Topical Terms:

194058
Artificial intelligence.

LC Class. No.: Q335

Dewey Class. No.: 006.3

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520 $a 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.
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