AI Foundation Tutorial

AI foundation and trend seminar tutorial with code

This repository contains materials for an [AI Foundation seminar](https://github.com/mac999/AI_foundation_tutorial/blob/main/AI_foundation_and_trend.pdf)([English version](https://github.com/mac999/AI_foundation_tutorial/blob/main/AI_foundation_and_trend(english).pdf)) with [syllabus](https://github.com/mac999/AI_foundation_tutorial/blob/main/AI_foundation_syllabus.pdf), covering fundamental concepts of AI, Machine Learning, Deep Learning, Natural Language Processing, [Transformers with Vibe coding](https://github.com/mac999/AI_foundation_tutorial/blob/main/Transformer_LLM_Vibecoding.pdf), and Large Language Models (LLMs), including agent-based approaches and related services. It is designed to provide hands-on experience, primarily utilizing Jupyter Notebooks. This is focusing on understanding the machine learning foundation model's concepts, mechanism, code, and development such as MLP, NLP, Transformer and LLM. In reference, you can learn [How to develop AI agent with LLM](https://github.com/mac999/LLM-RAG-Agent-Tutorial), [Computer Vision with Deep Learning](https://github.com/mac999/computer_vision_deeplearning) and [AI for Media Art](https://github.com/mac999/llm-media-art-demo) like below, deeply.

• How to develop AI agent with LLM: This repository contains 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. The colab code, source, presentation and reference with AI tools like below can be used for developing LLM, RAG and AI Agent.
• 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 learnin