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Total Size:
5.0 MB
Info Hash:
59314CA7B974B07C8F9C642F059E77BF6F5125DD
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April 20, 2026, 8:33 p.m.
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(Last updated: April 20, 2026, 8:38 p.m.)
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| Gupta R. Generative AI. Techniques, Models and Applications 2025.pdf | 5.0 MB |
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NOTE
SOURCE: Gupta R. Generative AI. Techniques, Models and Applications 2025
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COVER
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MEDIAINFO
Textbook in PDF format This book unlocks the full potential of modern AI systems through a meticulously structured exploration of concepts, techniques, and practical applications. This comprehensive book bridges theoretical foundations with real-world implementations, offering readers a unique perspective on the rapidly evolving field of generative technologies. From computational foundations to ethical considerations, the book systematically covers essential topics including foundation models, large-scale architectures, prompt engineering, and practical applications. The content seamlessly integrates complex technical concepts with industry-relevant examples, making it an invaluable resource for researchers, academicians, and practitioners. Distinguished by its balanced approach to theory and practice, this book serves as both a learning tool and reference guide. Readers will benefit from: Clear explanations of advanced concepts. Practical implementation insights. Current industry applications. Ethical framework discussions. Traditional programming and AI programming represent two distinct paradigms in the realm of software development, each with its unique approach to problem-solving and application development. These paradigms differ fundamentally in their methodologies, objectives, and capabilities, shaping the nature and scope of the applications they enable. Traditional programming is defined by a deterministic and rule-based approach, where developers explicitly code the logic and rules that dictate the behavior of the software. It relies on a clear, predefined set of instructions that the computer follows to perform specific tasks or solve specific problems. In contrast, AI programming is characterized by a probabilistic and learning-based approach, where models are trained to learn patterns and make decisions based on data. It leverages algorithms that can generalize from examples and adapt to new information, enabling the development of intelligent and adaptive applications. Whether you're conducting research, implementing solutions, or exploring the field, this book provides the knowledge necessary to understand and apply Generative AI technologies effectively while considering crucial aspects of security, privacy, and fairness. Introduction to Artificial Intelligence AI Paradigms Traditional Programming Versus AI Programming Computational Foundation of Generative AI Models Generative AI Techniques and Models Foundation Models Large Language Models Large Generative Models for Different Data Types Prompt Engineering Applications of Generative AI Models Ethics, Governance, Security and Privacy Biases and Fairness in LLMs
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