LLM RAG Agent Tutorial

LLM, RAG and AI agent development tutorial

This repository contains [Syllabus](https://github.com/mac999/LLM-RAG-Agent-Tutorial/blob/main/1-1.prepare/syllabus-llm-rag-agent.docx) for LLM(large language model), RAG(retrieval augmented generation), AI Agent and MCP(Model Context Protocol) class focusing on creative AI agent development, modeling, and computing as the viewpoint of usecase. This repository was developed for AI application practitioners and developers. The colab code, source, presentation and reference with AI tools like below can be used for developing LLM, RAG and AI Agent. If you want to know the LLM, RAG and AI Agent with MCP subjects and materials, refer to the below link.

• Syllabus and Presentation

If you need AI deep learning foundation, refer the below link.

• AI foundation and tutorial with code: This repository contains materials for an AI Foundation seminar, covering fundamental concepts of AI, Machine Learning, Deep Learning, Natural Language Processing, Transformers, and Large Language Models (LLMs), including agent-based approaches and related services. It is designed to provide hands-on experience, primarily utilizing Jupyter Notebooks.
• Computer Vision with Deep Learning: This course goes beyond simply running pre-existing code. The core objective is to foster a deep understanding by having you implement the internal mechanisms of key deep learning models—such as CNN, ResNet, R-CNN, and YOLO—from the ground up. With hands-on exercises in PyTorch and Keras, you will gain proficiency in translating complex theories into functional code.
• Multimodal AI Development Learning: