دانلود کتاب Generative AI and LLMs Natural Language Processing and Generative Adversarial Networks

Title Page
Copyright
Contents
Preface
About the Editors
List of Contributors
1 Unveiling the Power of Generative AI: A Journey into Large Language Models
1.1 Overview of Generative AI and Large Language Models
1.2 Fundamental Concepts
1.3 Algorithms Used in Generative Models
1.4 Text Generation
1.5 Pretraining and Fine-Tuning of LLM Models
1.6 Impact on Generative AI and LLM
1.7 Application of LLMs
1.8 Challenges and Limitations
1.9 Future Directions
1.10 Conclusion
References
2 Early Roots of Generative AI Models and LLM: A Diverse Landscape
2.1 Introduction to Rule-Based Approaches
2.2 Emergence of Statistical Language Models
2.3 Early Experiments on Neural Network
2.4 Pioneering Architectures in Language Modeling
2.5 Integration of Expert Systems with Language Models
2.6 Impact on Early Generative AI
2.7 Theoretical Foundations and Hybrid Approaches
2.8 Limitations and Challenges
2.9 Bridge to Modern Large Language Models (LLMs)
2.10 Conclusion
References
3 Generative AI Models and LLM: Training Techniques and Evaluation Metrics
3.1 Introduction
3.2 Generative AI Model and LLM Training Techniques
3.3 Variational Autoencoder
3.4 Transformer Models
3.5 LangChain
3.6 Diffusion Model
3.7 Flow-Based Models
3.8 Evaluation Metrics
3.9 Conclusion
References
4 Importance of Prompt Engineering in Generative AI Models
4.1 Introduction
4.2 Theoretical Underpinnings of Prompt Engineering
4.3 Methodologies in Prompt Engineering
4.4 Empirical Studies and Case Examples
4.5 Examining the Influence of Prompts: Multidisciplinary Views
4.6 Interdisciplinary Perspectives on Prompt Engineering
4.7 Future Directions and Challenges
4.7 Emerging Trends in Prompt Engineering
4.8 Prospects and Difficulties
4.9 Conclusion
References
5 LLM Pretraining Methods
5.1 Introduction
5.2 Steps for Training LLM Models
5.3 Study of Pretraining in LLM
5.4 Effect of Pretraining on LLM
5.5 Key Considerations for Pretraining LLM
5.6 Characteristics of LLM Pretraining
5.7 Some Use Cases of LLM Pretraining
5.8 Summary
References
6 LLM Fine-Tuning: Instruction and Parameter-Efficient Fine-Tuning (PEFT)
6.1 Introduction
6.2 LLM Fine-Tuning: Instruction and Parameter Efficient Fine-Tuning
6.3 Reinforcement Learning from Human Feedback (RLHF)
6.4 Parameter-Efficient Fine-Tuning (PEFT)
6.5 PEFT Methods
6.6 LoRA: Low-Rank Adaption Method
6.7 QLoRA: Quantized Low-Rank Adaption Method
6.8 Conclusion
References
7 Reinforcement Learning from Human Feedback (RLHF)
7.1 Introduction
7.2 Foundations of Reinforcement Learning
7.3 Transitioning to RLHF
7.4 Impact of RLHF on Tailoring LLMs: Case Studies
7.5 Ethical Considerations in RLHF for LLMs
7.6 RLHF Derivatives
7.7 Conclusion
References
8 Exploring the Applications on Generative AI and LLM
8.1 Overview to Generative AI
8.2 Meta Learning Fundamentals for Adaptive Scientific Modeling
8.3 Automatic Hypothesis Generation with Generative Models
8.4 Quantum Computing Concepts in Generative Models
8.5 Real-Time Collaboration with Generative Models
8.6 Implementation of Privacy-Preserving Techniques
8.7 Enhancing Scientific Visualization Techniques
8.8 Leveraging Blockchain for Trust and Transparency
8.9 Conclusion and Future Directions
References
9 Bias and Fairness in Generative AI
9.1 Introduction
9.2 Bias: Sources, Impact, and Mitigation Strategies
9.3 Fairness: Metrics and Mitigation Strategies
9.4 Conclusion
References
10 Future Directions and Open Problems in Generative AI
10.1 Introduction
10.2 Importance of Exploring GenAI
10.3 Improving Control and Interpretability in Generative AI
10.4 Ethical Challenges in Generative AI
10.5 Expanding Generative Frameworks
10.6 Semantic Gap
10.7 Innovative Architectures
10.8 Research Areas in Generative AI
10.9 Industry Perspectives and Case Studies
10.10 Conclusion
References
11 Optimizing Sustainable Project Management Life Cycle Using Generative AI Modeling
11.1 Introduction
11.2 Literature Review
11.3 Current Issues in Project/Product Life Cycle Management Using GenAI
11.4 Optimizing the GenAI Made for Edge Devices in the Near Future
11.5 Conclusion
References
12 Generative AI and LLM: Case Study in Finance
12.1 Introduction
12.2 Challenges and Ethical Considerations for Language Models in Finance
12.3 Major FinTech Models
12.4 Conclusion and Future Directions
References
13 Generative AI and LLM: Case Study in E-Commerce
13.1 Introduction
13.2 Significance of AI in E-Commerce
13.3 Case Studies
13.4 Implementation Strategies
13.5 Future Trends in E-Commerce
13.6 Conclusion
References
Index