A Beginner’s Guide to Generative AI
Springer International Publishing (Verlag)
978-3-031-84723-3 (ISBN)
This book is the essential guide for anyone curious about AI's creative power. In the rapidly evolving landscape of artificial intelligence, generative AI stands out as one of the most transformative technologies of our time. Designed for beginners and requiring no prior knowledge of AI, this book breaks down the fundamentals of generative AI, from text and image generation to the workings of models like ChatGPT and Google Bard. The authors provide step-by-step coverage of the essential concepts and techniques that power generative AI. From the basics of how machines learn to generate text and images, to the intricate workings of models like Transformers, ChatGPT, and Google Bard, readers will gain a solid foundation in AI's most cutting-edge tools. Rather than focusing on a single method, the authors introduce a spectrum of generative modeling techniques, including diffusion models, variational autoencoders, and transformers. This comprehensive exposure ensures readers will be well-prepared to understand and adapt to the rapidly evolving AI landscape. In addition, real-world applications of generative AI across various industries are explored including healthcare innovations, business analytics, and legal technology, and the authors provide practical insights and examples that show how generative AI is revolutionizing these fields.
Deepshikha Bhati is a Lecturer in the Department of Computer Science at Kent State University. Prior to her current role, she built a strong foundation in both industry and academia, holding various teaching and research positions. She earned her bachelor's and master's degrees in computer science from Dr. A. P. J. Abdul Kalam Technical University, India, and is currently pursuing her Ph.D. in computer science from Kent State University. Her academic journey includes deep expertise in Generative AI LLM Models, Explainable AI (XAI), Information Visualization, Image Processing, Deep Learning (DL), and Machine Learning (ML). Deepshikha has made significant contributions through her research in these areas, and her professional experience spans both practical problem-solving and innovative computational research. Her work has uniquely positioned her to bridge the gap between theoretical concepts and real-world applications, making her a recognized name in the field of AI and Data Science. Ms. Bhati is a member of IEEE, the IEEE Computer Society, and ACM.
Fnu Neha is a Ph.D. candidate in the Department of Computer Science at Kent State University, with over six years of experience in research and teaching, particularly in artificial intelligence (AI), deep learning, and databases. Her Ph.D. research focuses on developing deep learning techniques and AI-based software to analyze renal CT scans and correlate them with radiography and biopsy-based features for the accurate classification of small renal masses and subtypes of renal cell carcinoma (RCC).
Angela Guercio, Ph.D., is an Associate Professor of Computer Science at Kent State University, where she has worked for 19 years. Prior to joining KSU, she served as an Assistant Professor at Hiram College for 3 years and as a Senior Research Associate at the University of Salerno, Italy, for 16 years. Dr. Guercio received her Ph.D. in Computer Science from Kent State University in 2004, a Master of Science in Computer and Information Sciences from the Knowledge Systems Institute in Chicago in 2000, and a Laurea Degree of Doctor in Computer Science "cum laude" from the University of Salerno, Italy, in 1984. Her research interests include Smart e-Education and AI, Big Data, Data Mining, Software Engineering, Visual Languages, Human-Machine Interaction, and Multimedia Computing. She has co-authored numerous papers published in scientific journals and refereed international conferences. Dr. Guercio has received multiple research awards and fellowships for her work. She serves as Associate editor of the Journal of Visual Languages and Sentient Systems and has served a member of the editorial board of the International Journal on Advances in Networks and Services. Additionally, she is a reviewer for several high-impact international research journals and has chaired and participated in the organization of several international conferences, as well as co-edited special issues of international journals. Prof. Guercio is a member of IEEE, the IEEE Computer Society, and ACM.
Md Amiruzzaman, Ph.D., is an Assistant Professor in the Department of Computer Science at West Chester University. Before joining WCU, he worked as a software developer for almost 10 years for several companies. He has also held the position of Assistant Professor at Kent State University. He has completed a Bachelor's Degree in Computer Science from National University. Along with that, he has completed four Master's degrees with major in Computer Engineering in 2008 from Sejong University, Computer Science in 2011 from Kent State University (also, partly at Korea University), and Technology in 2015, also from Kent State University, and a Master's in Cybersecurity in 2023 from Georgia Institute of Technology. He received his Ph.D. degrees from Kent State University in 2016 (Mathematics Edu), 2019 (Evaluation and Measurement) and 2021 (Computer Science). In the past, he has worked as a Research Ass
Introduction to Generative AI.- Evolution of Neural Networks to Large Language Models.- LLMs and Transformers.- The ChatGPT Architecture: An In-Depth Exploration of OpenAI.- Google Bard and Beyond.- Diffusion Model and Generative AI for Images.- Setting Up the Environment and Implementing LLMs.- ChatGPT Use Cases.
| Erscheinungsdatum | 23.07.2025 |
|---|---|
| Reihe/Serie | Synthesis Lectures on Computer Science |
| Zusatzinfo | XIX, 236 p. 63 illus., 60 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 168 x 240 mm |
| Themenwelt | Geisteswissenschaften ► Sprach- / Literaturwissenschaft ► Sprachwissenschaft |
| Mathematik / Informatik ► Informatik ► Datenbanken | |
| Mathematik / Informatik ► Informatik ► Theorie / Studium | |
| Schlagworte | AI for Content Creation • Artificial Intelligence • ChatGPT Architecture • diffusion models • Ethical considerations in AI • generative AI • Google Bard vs ChatGPT • Large Language Models (LLMs) • machine learning • Neural Network Evolution • Text Generation with AI • Transformers in AI |
| ISBN-10 | 3-031-84723-7 / 3031847237 |
| ISBN-13 | 978-3-031-84723-3 / 9783031847233 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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