Supercharge AI with Knowledge Graphs: RAG System Mastery NEW

Übersicht

Are you ready to take your AI skills to the next level? Welcome to "Supercharge AI with Knowledge Graphs:

RAG System Mastery", the ultimate course designed to unlock the full potential of Large Language Models (LLMs) using cutting-edge techniques in Knowledge Graphs and Retrieval-Augmented Generation (RAG) systems.

Lehrplan

• Introduction to Knowledge Graphs
Definition and key concepts
Importance and applications in AI
• Fundamentals of Large Language Models (LLMs)
Overview of LLM architectures
Capabilities and limitations
• Building Knowledge Graphs
Data sources and acquisition
Graph databases and tools
Semantic web technologies
• Retrieval-Augmented Generation (RAG) Systems
RAG architecture and components
Advantages over traditional LLMs
Case studies and use cases
• Integrating Knowledge Graphs with LLMs
Techniques for enhancing LLMs with knowledge graphs
Querying and updating graphs in real-time
• Advanced RAG Techniques
Customizing retrieval mechanisms
Handling large-scale datasets
Optimizing for performance
• Hands-On Projects
Building a simple RAG system
Real-world applications and problem-solving
Project presentations and feedback
• Tools and Technologies
Overview of popular tools for building knowledge graphs (e.g., Neo4j, RDF frameworks)
Integration tools for RAG systems (e.g., Haystack, Faiss)
• Ethical Considerations and Future Trends
Bias mitigation in knowledge systems
Future developments in knowledge graphs and AI
• Course Review and Next Steps
Summary of key concepts
Further reading and resources
Paths for continued learning and career development in AI and knowledge technologies

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Unterrichtet von

Paulo Dichone | Software Engineer, AWS Cloud Practitioner & Instructor

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Fachgebiete

Data Science