Generative AI in R

Akansha Singh is a professor in the School of Computer Science and Engineering at Bennett University, Greater Noida, India. With an impressive academic background that includes a B.Tech, M. Tech, and a Ph.D. in Computer Science from IIT Roorkee, her expertise lies primarily in image processing, deep learning and machine learning. Dr. Singh's academic contributions extend beyond teaching; she has played significant roles as an Associate Editor and Guest Editor for several scholarly journals. She has written more than 100 research papers in reputed journals, conferences, and books and authored more than 30 books in advanced computer science areas. Her dedication to research is evident through her leadership in government-funded projects as a Principal Investigator. Her research interests encompass a broad range of topics, including image processing, remote sensing, IoT, and machine learning. Krishna Kant Singh serves as the Director of the Delhi Technical Campus, Greater Noida, India, bringing a wealth of teaching and research experience to his role. He hold multiple degrees, including a B. Tech, M. Tech, MS, and a Ph.D. from IIT Roorkee, all focused on image processing and Machine Learning. Dr. Singh has authored over 140 research papers in esteemed Scopus and SCIE indexed journals, along with 25 technical books, showcasing his profound impact on the field. He is also the associate editor of IEEE ACCESS and many other journals of high repute. He has also served as a Guest Editor for Open Computer Science, Wireless Personal Communications, Complex and Intelligent systems, and many other journals. Additionally, his involvement in the Editorial Board of Applied Computing and Geosciences (Elsevier) highlights his significant contributions to academia and research.

• 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.