فهرست مطالب

• Title Page
• Copyright and Credits
• Dedication
• Disclaimer
• Contributors
• Table of Contents
• Preface
• Part 1: The AI Agent Engine: From Text to Large Language Models
• Chapter 1: Analyzing Text Data with Deep Learning
• Technical requirements
• Representing text for AI
• Embedding, application, and representation
• RNNs, LSTMs, GRUs, and CNNs for text
• Performing sentiment analysis with embedding and deep learning
• Summary
• Chapter 2: The Transformer: The Model Behind the Modern AI Revolution
• Technical requirements
• Exploring attention and self-attention
• Introducing the transformer model
• Training a transformer
• Exploring masked language modeling
• Visualizing internal mechanisms
• Applying a transformer
• Summary
• Chapter 3: Exploring LLMs as a Powerful AI Engine
• Technical requirements
• Discovering the evolution of LLMs
• Instruction tuning, fine-tuning, and alignment
• Exploring smaller and more efficient LLMs
• Exploring multimodal models
• Understanding hallucinations and ethical and legal issues
• Prompt engineering
• Summary
• Further reading
• Part 2: AI Agents and Retrieval of Knowledge
• Chapter 4: Building a Web Scraping Agent with an LLM
• Technical requirements
• Understanding the brain, perception, and action paradigm
• Classifying AI agents
• Understanding the abilities of single-agent and multiple-agent systems
• Exploring the principal libraries
• Creating an agent to search the web
• Summary
• Further reading
• Chapter 5: Extending Your Agent with RAG to Prevent Hallucinations
• Technical requirements
• Exploring naïve RAG
• Retrieval, optimization, and augmentation
• Evaluating the output
• Comparison between RAG and fine-tuning
• Using RAG to build a movie recommendation agent
• Summary
• Further reading
• Chapter 6: Advanced RAG Techniques for Information Retrieval and Augmentation
• Technical requirements
• Discussing naïve RAG issues
• Exploring the advanced RAG pipeline
• Modular RAG and its integration with other systems
• Implementing an advanced RAG pipeline
• Understanding the scalability and performance of RAG
• Open questions and future perspectives
• Summary
• Further reading
• Chapter 7: Creating and Connecting a Knowledge Graph to an AI Agent
• Technical requirements
• Introduction to knowledge graphs
• Creating a knowledge graph with your LLM
• Retrieving information with a knowledge graph and an LLM
• Understanding graph reasoning
• Ongoing challenges in knowledge graphs and GraphRAG
• Summary
• Further reading
• Chapter 8: Reinforcement Learning and AI Agents
• Technical requirements
• Introduction to reinforcement learning
• Deep reinforcement learning
• LLM interactions with RL models
• Key takeaways
• Summary
• Further reading
• Part 3: Creating Sophisticated AI to Solve Complex Scenarios
• Chapter 9: Creating Single- and Multi-Agent Systems
• Technical requirements
• Introduction to autonomous agents
• Working with HuggingGPT
• Multi-agent system
• SaaS, MaaS, DaaS, and RaaS
• Summary
• Further reading
• Chapter 10: Building an AI Agent Application
• Technical requirements
• Introduction to Streamlit
• Developing our frontend with Streamlit
• Creating an application with Streamlit and AI agents
• Machine learning operations and LLM operations
• Asynchronous programming
• Docker
• Summary
• Further reading
• Chapter 11: The Future Ahead
• AI agents in healthcare
• AI agents in other sectors
• Challenges and open questions
• Summary
• Further reading
• Index
• About Packt
• Other Books You May Enjoy